[SPARK-45999][PS] Use dedicated PandasProduct in cumprod

### What changes were proposed in this pull request?
Use dedicated `PandasProduct` in `cumprod`

### Why are the changes needed?
to be consistent with the `prod`

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?
ci

### Was this patch authored or co-authored using generative AI tooling?
no

Closes #43901 from zhengruifeng/ps_cumprod.

Authored-by: Ruifeng Zheng <ruifengz@apache.org>
Signed-off-by: Ruifeng Zheng <ruifengz@apache.org>

python/pyspark/pandas/generic.py

@@ -331,8 +331,6 @@ def cumsum(self: FrameLike, skipna: bool = True) -> FrameLike:
         return self._apply_series_op(lambda psser: psser._cumsum(skipna), should_resolve=True)
 
     # TODO: add 'axis' parameter
-    # TODO: use pandas_udf to support negative values and other options later
-    #  other window except unbounded ones is supported as of Spark 3.0.
     def cumprod(self: FrameLike, skipna: bool = True) -> FrameLike:
         """
         Return cumulative product over a DataFrame or Series axis.

python/pyspark/pandas/series.py

@@ -62,7 +62,6 @@
     DoubleType,
     FloatType,
     IntegerType,
-    IntegralType,
     LongType,
     NumericType,
     Row,
@@ -6973,36 +6972,17 @@ def _cumsum(self, skipna: bool, part_cols: Sequence["ColumnOrName"] = ()) -> "Se
         return psser._cum(F.sum, skipna, part_cols)
 
     def _cumprod(self, skipna: bool, part_cols: Sequence["ColumnOrName"] = ()) -> "Series":
-        if isinstance(self.spark.data_type, BooleanType):
-            scol = self._cum(
-                lambda scol: F.min(F.coalesce(scol, F.lit(True))), skipna, part_cols
-            ).spark.column.cast(LongType())
-        elif isinstance(self.spark.data_type, NumericType):
-            num_zeros = self._cum(
-                lambda scol: F.sum(F.when(scol == 0, 1).otherwise(0)), skipna, part_cols
-            ).spark.column
-            num_negatives = self._cum(
-                lambda scol: F.sum(F.when(scol < 0, 1).otherwise(0)), skipna, part_cols
-            ).spark.column
-            sign = F.when(num_negatives % 2 == 0, 1).otherwise(-1)
-
-            abs_prod = F.exp(
-                self._cum(lambda scol: F.sum(F.log(F.abs(scol))), skipna, part_cols).spark.column
-            )
-
-            scol = F.when(num_zeros > 0, 0).otherwise(sign * abs_prod)
-
-            if isinstance(self.spark.data_type, IntegralType):
-                scol = F.round(scol).cast(LongType())
-        else:
+        psser = self
+        if isinstance(psser.spark.data_type, BooleanType):
+            psser = psser.spark.transform(lambda scol: scol.cast(LongType()))
+        elif not isinstance(psser.spark.data_type, NumericType):
             raise TypeError(
                 "Could not convert {} ({}) to numeric".format(
-                    spark_type_to_pandas_dtype(self.spark.data_type),
-                    self.spark.data_type.simpleString(),
+                    spark_type_to_pandas_dtype(psser.spark.data_type),
+                    psser.spark.data_type.simpleString(),
                 )
             )
-
-        return self._with_new_scol(scol)
+        return psser._cum(lambda c: SF.product(c, skipna), skipna, part_cols)
 
     # ----------------------------------------------------------------------
     # Accessor Methods