Describe the bug

When using the SimpleImputer with a pyarrow-backed pandas DataFrame, any float/integer data is converted to None/float64 instead.
This causes the imputer to be fitted to float64, crashing on a dtype assertion when passing it a numpy-backed int32 DataFrame after fitting.

The flow is the following:

1. The imputer calls _validate_input:
   

   scikit-learn/sklearn/impute/_base.py

   Line 319 in 675736a

   def _validate_input(self, X, in_fit):

2. This calls validate_data:
   

   scikit-learn/sklearn/impute/_base.py

   Lines 344 to 353 in 675736a

   	X = validate_data(
   	self,
   	X,
   	reset=in_fit,
   	accept_sparse="csc",
   	dtype=dtype,
   	force_writeable=True if not in_fit else None,
   	ensure_all_finite=ensure_all_finite,
   	copy=self.copy,
   	)

3. This calls check_array:
   

   scikit-learn/sklearn/utils/validation.py

   Lines 2951 to 2952 in 675736a

   	elif not no_val_X and no_val_y:
   	out = check_array(X, input_name="X", **check_params)

4. Our input is a pandas dataframe:
   

   scikit-learn/sklearn/utils/validation.py

   Line 909 in 675736a

   if hasattr(array, "dtypes") and hasattr(array.dtypes, "__array__"):

5. This now checks if the dtypes need to be converted:
   

   scikit-learn/sklearn/utils/validation.py

   Lines 925 to 927 in 675736a

   	pandas_requires_conversion = any(
   	_pandas_dtype_needs_early_conversion(i) for i in dtypes_orig
   	)

6. Our input is backed by an extension array and int32[pyarrow] is an integer datatype, so we return True here:
   

   scikit-learn/sklearn/utils/validation.py

   Lines 714 to 724 in 675736a

   	if isinstance(pd_dtype, SparseDtype) or not is_extension_array_dtype(pd_dtype):
   	# Sparse arrays will be converted later in `check_array`
   	# Only handle extension arrays for integer and floats
   	return False
   	elif is_float_dtype(pd_dtype):
   	# Float ndarrays can normally support nans. They need to be converted
   	# first to map pd.NA to np.nan
   	return True
   	elif is_integer_dtype(pd_dtype):
   	# XXX: Warn when converting from a high integer to a float
   	return True

7. Finally we pass the "needs conversion" check and convert the dataframe to dtype (which is None here, which apparently means float64):
   

   scikit-learn/sklearn/utils/validation.py

   Lines 966 to 971 in 675736a

   	if pandas_requires_conversion:
   	# pandas dataframe requires conversion earlier to handle extension dtypes with
   	# nans
   	# Use the original dtype for conversion if dtype is None
   	new_dtype = dtype_orig if dtype is None else dtype
   	array = array.astype(new_dtype)

Steps/Code to Reproduce

import polars as pl
from sklearn.impute import SimpleImputer
import numpy as np

print(
    SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0)
      .fit(pl.DataFrame({"a": [10]}, schema={"a": pl.Int32}).to_pandas(use_pyarrow_extension_array=False))
      ._fit_dtype
)
# prints dtype('int32'), as expected

print(
    SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0)
      .fit(pl.DataFrame({"a": [10]}, schema={"a": pl.Int32}).to_pandas(use_pyarrow_extension_array=True))
      ._fit_dtype
)
# prints dtype('float64') (!!)

Expected Results

Both imputers should be fitted with int32 values.

Actual Results

The imputer using the pyarrow extension array is fitted with float64.

This causes crashes when using the Imputer with normal int32 columns backed by numpy, as they won't be converted and therefore the dtypes differ.

Versions

System:
    python: 3.12.9 (main, Mar 11 2025, 17:26:57) [Clang 20.1.0 ]
executable: /tmp/scikit/.venv/bin/python3
   machine: Linux-6.14.4-arch1-2-x86_64-with-glibc2.41

Python dependencies:
      sklearn: 1.6.1
          pip: None
   setuptools: None
        numpy: 2.2.5
        scipy: 1.15.3
       Cython: None
       pandas: 2.2.3
   matplotlib: None
       joblib: 1.5.0
threadpoolctl: 3.6.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 16
         prefix: libscipy_openblas
       filepath: /tmp/scikit/.venv/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-6bb31eeb.so
        version: 0.3.28
threading_layer: pthreads
   architecture: Haswell

       user_api: blas
   internal_api: openblas
    num_threads: 16
         prefix: libscipy_openblas
       filepath: /tmp/scikit/.venv/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-68440149.so
        version: 0.3.28
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 16
         prefix: libgomp
       filepath: /tmp/scikit/.venv/lib/python3.12/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None