Migrate LogTransformer/LogCpTransformer to narwhals, add polars support

[solegalli]

LogTransformer's C parameter (scalar/dict/"auto") makes this more than a pure elementwise op like the other sibling transformers: "auto" needs a per-variable min reduction, and the shift C can vary per column. Extract the transform columns to a single numpy array via narwhals' to_numpy(), compute the per-column shift with np.where(mins > 0, 0, abs(mins) + 1) for "auto", broadcast a dict C_ into a numpy array ordered to match variables_, apply np.log/np.log10 once, reassign via nw.new_series + with_columns. LogCpTransformer is a subclass of LogTransformer (same file, no separate work needed).

Benchmarked narwhals-on-pandas vs the old pandas-native .loc-assignment across 10k-100k rows and 1-10 columns: narwhals-on-pandas ran at 0.53x-0.87x of the old runtime (avg 0.67x, ~1.5x faster), narwhals-on-polars faster still (avg 0.59x, ~1.7x faster) - consistent with every other sibling in this module, so no pandas/polars branch was added; transform() and inverse_transform() share one merged narwhals path.

Verified pandas/polars parity directly (C=int/dict/"auto", both bases, including inverse_transform) since pyarrow isn't installed in this env, so narwhals' to_pandas()/to_native() round-trips weren't usable for comparison - compared to_dict(as_series=False) output instead.

Rewrote test_log_transformer.py and test_logcp_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/DATA_C dicts, same pattern as test_reciprocal_transformer.py. All expected values recomputed and verified against actual output.

Found one doc/output drift caused by the migration itself: LogCpTransformer.rst showed {'MedInc': 0, 'HouseAge': 0} for C="auto" on strictly-positive variables, but casting the whole numpy array to float (needed for the mixed positive/non-positive np.where computation) means the "no shift needed" case is now 0.0, not int 0 - updated the doc to match. Cosmetic only: dict equality (0.0 == 0) means no test assertion needed updating. Verified every other code example already in LogTransformer.rst and LogCpTransformer.rst against current output (fetch_california_housing/ load_diabetes - no network needed, both ship with scikit-learn) - all matched exactly. Added a verified "With polars" section to each doc.

flake8/mypy clean on feature_engine/transformation/log.py; sphinx-build -W clean (only the pre-existing linkcode_resolve warning, unrelated); log.py imports standalone with pandas import blocked at the builtins level; full tests/test_transformation suite shows the same 8 pre-existing failures as the pre-migration baseline (numpy-array input rejected by check_X, a base-branch issue in dataframe_checks.py predating this work, unrelated to log.py) and zero new failures.