FIX-#6899: Avoid sending lazy categorical proxies to workers (#6900)

Signed-off-by: Dmitry Chigarev <dmitry.chigarev@intel.com>

modin/core/dataframe/pandas/dataframe/dataframe.py

@@ -3852,7 +3852,22 @@ def apply_func(df):  # pragma: no cover
             #   2. The second one works slower, but only gathers light pandas.Index objects,
             #      so there should be less stress on the network.
             if add_missing_cats or not IsRayCluster.get():
-                original_dtypes = self.dtypes if self.has_materialized_dtypes else None
+                if self.has_materialized_dtypes:
+                    original_dtypes = pandas.Series(
+                        {
+                            # lazy proxies hold a reference to another modin's DataFrame which can be
+                            # a problem during serialization, in this scenario we don't need actual
+                            # categorical values, so a "category" string will be enough
+                            name: (
+                                "category"
+                                if isinstance(dtype, LazyProxyCategoricalDtype)
+                                else dtype
+                            )
+                            for name, dtype in self.dtypes.items()
+                        }
+                    )
+                else:
+                    original_dtypes = None
 
                 def compute_aligned_columns(*dfs, initial_columns=None):
                     """Take row partitions, filter empty ones, and return joined columns for them."""

modin/pandas/test/test_groupby.py

@@ -3164,14 +3164,6 @@ def test_groupby_agg_provided_callable_warning():
             pandas_groupby.agg(func)
 
 
-def _apply_transform(df):
-    if len(df) == 0:
-        df = df.copy()
-        df.loc[0] = 10
-        return df.squeeze()
-    return df.sum()
-
-
 @pytest.mark.parametrize(
     "modify_config", [{RangePartitioningGroupby: True}], indirect=True
 )
@@ -3183,7 +3175,16 @@ def _apply_transform(df):
         pytest.param(lambda grp: grp.sum(), id="sum"),
         pytest.param(lambda grp: grp.size(), id="size"),
         pytest.param(lambda grp: grp.apply(lambda df: df.sum()), id="apply_sum"),
-        pytest.param(lambda grp: grp.apply(_apply_transform), id="apply_transform"),
+        pytest.param(
+            lambda grp: grp.apply(
+                lambda df: (
+                    df.sum()
+                    if len(df) > 0
+                    else pandas.Series([10] * len(df.columns), index=df.columns)
+                )
+            ),
+            id="apply_transform",
+        ),
     ],
 )
 @pytest.mark.parametrize(