PERF: Use Arrow path in _factorize_keys for all Arrow Dtypes (#63435)

Author: fangchenli

doc/source/whatsnew/v3.0.0.rst

@@ -1091,13 +1091,15 @@ Performance improvements
 - Performance improvement in :meth:`CategoricalDtype.update_dtype` when ``dtype`` is a :class:`CategoricalDtype` with non ``None`` categories and ordered (:issue:`59647`)
 - Performance improvement in :meth:`DataFrame.__getitem__` when ``key`` is a :class:`DataFrame` with many columns (:issue:`61010`)
 - Performance improvement in :meth:`DataFrame.astype` when converting to extension floating dtypes, e.g. "Float64" (:issue:`60066`)
+- Performance improvement in :meth:`DataFrame.merge` by using Arrow-native path for all Arrow-backed dtypes (:issue:`63435`)
 - Performance improvement in :meth:`DataFrame.stack` when using ``future_stack=True`` and the DataFrame does not have a :class:`MultiIndex` (:issue:`58391`)
 - Performance improvement in :meth:`DataFrame.to_hdf` avoid unnecessary reopenings of the HDF5 file to speedup data addition to files with a very large number of groups . (:issue:`58248`)
 - Performance improvement in :meth:`DataFrame.where` when ``cond`` is a :class:`DataFrame` with many columns (:issue:`61010`)
 - Performance improvement in ``DataFrameGroupBy.__len__`` and ``SeriesGroupBy.__len__`` (:issue:`57595`)
 - Performance improvement in indexing operations for string dtypes (:issue:`56997`)
 - Performance improvement in unary methods on a :class:`RangeIndex` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57825`)
 
+
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.bug_fixes:
 

pandas/core/reshape/merge.py

@@ -2832,7 +2832,7 @@ def _factorize_keys(
         rk = ensure_int64(rk.codes)
 
     elif isinstance(lk, ExtensionArray) and lk.dtype == rk.dtype:
-        if (isinstance(lk.dtype, ArrowDtype) and is_string_dtype(lk.dtype)) or (
+        if isinstance(lk.dtype, ArrowDtype) or (
             isinstance(lk.dtype, StringDtype) and lk.dtype.storage == "pyarrow"
         ):
             import pyarrow as pa