Migrate YeoJohnsonTransformer to narwhals, add polars support

[solegalli]

fit() learns a per-column lambda via scipy.stats.yeojohnson's optimizer search - that search dominates the runtime (10-800x the cost of transform/inverse_transform at 10k-100k rows), so the narwhals extraction overhead there is noise: benchmarked narwhals-on-pandas vs old pandas-native fit at 10k-100k rows x 1-10 cols and got ~0.97x-1.01x of the old runtime, i.e. parity.

transform() still calls scipy.stats.yeojohnson per column (its formula branches on a scalar lmbda, so it can't be vectorized across columns with different lambdas in one call) but now extracts to a single numpy array via to_numpy() first and reassigns via nw.new_series + with_columns. Benchmarked narwhals-on-pandas vs old .loc-assignment: 0.97x-1.2x of old runtime at realistic sizes (>=50k rows), degrading to ~1.9x at the smallest case tested (10k rows x 1 col) where both absolute times are sub-millisecond and dominated by call overhead rather than real work - in line with every other merged sibling in this module (Power/Reciprocal), so no pandas/polars branch was added.

inverse_transform()'s hand-written pos/neg-lambda formula no longer needs pandas.Series/.loc boolean-mask assignment - it now operates on a extracted numpy array per column instead, which benchmarked 1.4x-3.5x faster than the old code across the same size grid, on top of adding polars support for free.

Rewrote test_yeojohnson_transformer.py to one parametrized test per behavior over pandas/polars input (previously pandas-only, relying on the global df_vartypes/df_na fixtures - replaced with local DATA/DATA_NA dicts, same pattern as test_reciprocal_transformer.py). All expected values recomputed and verified against actual output. Kept test_inverse_with_non_linear_index pandas-only since it specifically exercises pandas Index-preserving behaviour with no polars equivalent.

Found the class docstring's pandas example values were already stale before this migration (verified against git-stashed pre-migration code: old code prints -267042.661354 for the first row, not the documented -267042.906453) - a scipy version drift in the yeojohnson lambda optimizer, unrelated to this migration. Fixed both the pandas example and added a verified "With polars" section to
docs/user_guide/transformation/YeoJohnsonTransformer.rst.

Left the pre-existing Ames-housing fetch_openml walkthrough in the docs untouched: the OpenML house_prices snapshot/sklearn parser now returns different row order than when the doc was written (X_train.head() shows different indices/houses than documented), which is upstream drift unrelated to this migration and would require regenerating the large embedded data table and histogram PNGs to fix properly - flagging for a separate follow-up rather than doing it here.

Verified: pytest tests/test_transformation (140 passed, same 8 pre-existing check_estimator failures as baseline, zero new failures), flake8 and mypy clean on the touched files, sphinx -W build clean (only the pre-existing unrelated linkcode_resolve warning), and the module imports standalone with pandas import blocked.