Categorical Features Support in T/R learners

[aman-coder03]

Proposed changes

Fixes #825
when a pd.DataFrame containing dtype="category" columns is passed to any meta-learner, convert_pd_to_np() was calling .to_numpy() on it, which strips all dtype information. By the time the underlying learner (e.g. XGBoost with enable_categorical=True) received the data, the categorical columns were gone and it would error out.

Two changes fix this...

1. convert_pd_to_np() in utils.py now skips the numpy conversion for DataFrames that contain at least one categorical column, passing them through as-is
2. BaseTLearner in tlearner.py no longer converts X to numpy in fit, predict, fit_predict, and estimate_ate
   only treatment and y are converted, since those are used for masking and arithmetic. X is left alone so dtypes survive all the way to the learner

existing behavior is fully preserved, DataFrames without categorical columns still get converted to numpy as before.

NOTE: XGBoost 3.x only supports integer-coded categoricals (pd.Categorical([0, 1, 2, ...])), not string valued ones (pd.Categorical(['a', 'b', 'c', ...])). This is an XGBoost limitation, not a causalml one

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