This PR sets up a config parameter sparse_interface to indicate "sparray" or "spmatrix" outputs, as suggested in #26418.

The first commit sets everything up and implements the system for a few modules. Please take a look and provide feedback for whether this is the way to proceed. The next commit(s) will implement these same style of changes throughout the library. If you would prefer they be in separate PRs let me know. (I'll keep Draft status until there is feedback and the full library is convered.)

More specifically, this PR does the following:

• adds sparse_interface to the config parameters. (I think this name is better than sparse_format because "format" means csr/coo/lil, etc in the sparse world.) The values it can hold are "sparray" or "spmatrix". Update config tests accordingly.
• adds utils._sparse.py with (private) helper functions. The difference is how much checking is done. Tests added too.
  • _as_sparse(x_sparse), raises unless sparse input. converts to interface chosen by config.
  • _select_interface_if_sparse(x), allows dense input with no action, sparse input uses _as_sparse.
  • one-line convenience functions: _convert_from_spmatrix_to_sparray(x) and _convert_from_sparray_to_spmatrix(x)
• updates the following modules to use this helper utility to return or store newly created sparse objects.
  • sklearn/feature_selection/text.py and adapts tests.
  • sklearn/linear_model/_coordinate_descent.py no tests change needed.
  • sklearn/manifold/_locally_linear.py no tests change needed.

@thomasjpfan can you see if this does what you had in mind? I tried to pick modules that cover returning sparse, setting estimators to hold sparse, and transforming to sparse, so you can see how this would work.

Let me know if you think _as_sparse should be a public function, and if my approach aligns with how you want it. The next steps for this PR are to repeat these type of changes throughout the library.