[Data] - Only return selected data columns in hive partitioned parquet files (#60236)

## Description
Returning `None` when you don't have partition_columns selects all the
partitions which is not the right behavior. Returning `[]` when no
partition columns are selected.

## Related issues
Closes #60215 

## Additional information
> Optional: Add implementation details, API changes, usage examples,
screenshots, etc.

---------

Signed-off-by: Goutam <goutam@anyscale.com>

Author: goutamvenkat-anyscale

python/ray/data/_internal/datasource/parquet_datasource.py

@@ -616,7 +616,11 @@ def _get_partition_columns(self) -> Optional[List[str]]:
             return None
 
         if not self._partition_columns:
-            return None
+            # If a projection is active but the dataset has no partition columns,
+            # then no partition columns should be included in the output.
+            # Returning [] ensures that no partition columns are added,
+            # `None` is interpreted as including all partition columns.
+            return []
 
         # Extract partition columns that are in the projection map
         partition_cols = [

python/ray/data/tests/datasource/test_parquet.py

@@ -391,6 +391,45 @@ def test_parquet_read_partitioned_with_columns(
     ]
 
 
+def test_parquet_read_partitioned_excludes_unrequested_partition_columns(
+    ray_start_regular_shared, tmp_path
+):
+    """Test that partition columns are excluded when not explicitly requested.
+
+    This is a regression test to ensure that when a user uses select_columns()
+    with only data columns, partition columns are NOT automatically included.
+    """
+    table = pa.table(
+        {
+            "partition_col0": [1, 1, 2, 2],
+            "partition_col1": ["a", "a", "b", "b"],
+            "data_col0": [10.5, 20.3, 30.2, 25.8],
+            "data_col1": [100, 200, 300, 400],
+        }
+    )
+
+    pq.write_to_dataset(
+        table,
+        root_path=tmp_path,
+        partition_cols=["partition_col0", "partition_col1"],
+    )
+
+    # Request only data columns excluding partition columns
+    ds = ray.data.read_parquet(
+        tmp_path,
+        columns=["data_col0"],
+        partitioning=Partitioning("hive"),
+    )
+
+    # Verify only the requested column is present
+    assert ds.columns() == ["data_col0"]
+
+    # Verify the data is correct
+    result_df = ds.to_pandas()
+    expected_df = pd.DataFrame({"data_col0": [10.5, 20.3, 25.8, 30.2]})
+    assert rows_same(result_df, expected_df)
+
+
 @pytest.mark.parametrize(
     "fs,data_path",
     [