Migrate BoxCoxTransformer to narwhals, add polars support

[solegalli]

fit()'s lambda search is per-column and not vectorizable (scipy.stats.boxcox with lmbda=None does per-column MLE optimization), while transform()/ inverse_transform() are pure elementwise math once lambdas are known - scipy.special.boxcox/inv_boxcox are ufuncs that broadcast a per-column lambda array against a 2D values array, so both methods extract via narwhals' to_numpy() once and apply a single batched call, same precedent as PowerTransformer (merged, not split).

Benchmarked pandas-native vs narwhals-on-pandas vs narwhals-on-polars at 10k/50k/100k rows x 1/2/10 columns, fit and transform measured separately since they're different cost centers:

• fit(): scipy's lambda-search optimization dominates total cost by 2-3 orders of magnitude over transform() (e.g. 10k rows/1 col: ~12.6ms fit vs ~0.1ms transform). narwhals overhead there is noise (<1% at every size/column combination tested).
• transform(): narwhals-on-pandas adds a small absolute overhead at tiny sizes (10k rows/1 col: 0.10ms old vs 0.27ms narwhals-loop/0.27ms narwhals-batched) but this shrinks to parity or better by 100k rows (9.03ms old vs 8.90ms narwhals-batched-pandas). Given fit() so overwhelmingly dominates real-world cost, a pandas/polars split for transform() would be real complexity for no measurable benefit - merged into a single narwhals path for both methods, matching every sibling transformer migrated in this module so far.

Rewrote test_boxcox_transformer.py to one parametrized test per behavior over make_df=[pd.DataFrame, pl.DataFrame], replacing the pandas-only df_vartypes/df_na fixtures with local DATA/DATA_NA dicts (same convention as test_relative_features.py). All expected values verified against actual output on both backends - identical.

docs/user_guide/transformation/BoxCoxTransformer.rst's main walkthrough uses fetch_openml against the Ames house-prices dataset; this sandbox has no network access (SSL/DNS blocked), so that section's numbers are UNVERIFIED against current output - flagging per instructions rather than silently skipping. Added a fully-verified "With polars" section using simple synthetic data, following the PowerTransformer precedent.

Verified: pytest tests/test_transformation (136 passed, same 8 pre-existing check_estimator failures as the unmigrated baseline, none new - confirmed those predate this change and affect all 8 transformers in the module, including ones not yet migrated); flake8 feature_engine tests clean; mypy feature_engine/transformation/boxcox.py clean; sphinx-build -W clean aside from the pre-existing unrelated linkcode_resolve warning (confirmed identical on the unmigrated base branch); boxcox.py and its full import chain load standalone with pandas import blocked.