[SPARK-45018][PYTHON][CONNECT] Add CalendarIntervalType to Python Client

### What changes were proposed in this pull request?
Add CalendarIntervalType to Python Client

### Why are the changes needed?
for feature parity

### Does this PR introduce _any_ user-facing change?
yes

before this PR:
```
In [1]: from pyspark.sql import functions as sf

In [2]: spark.range(1).select(sf.make_interval(sf.lit(1))).schema
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
Cell In[2], line 1
----> 1 spark.range(1).select(sf.make_interval(sf.lit(1))).schema

File ~/Dev/spark/python/pyspark/sql/connect/dataframe.py:1687, in DataFrame.schema(self)
   1685     if self._session is None:
   1686         raise Exception("Cannot analyze without SparkSession.")
-> 1687     return self._session.client.schema(query)
   1688 else:
   1689     raise Exception("Empty plan.")

...

Exception: Unsupported data type calendar_interval
```

after this PR:
```
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 4.0.0.dev0
      /_/

Using Python version 3.10.11 (main, May 17 2023 14:30:36)
Client connected to the Spark Connect server at localhost
SparkSession available as 'spark'.

In [1]: from pyspark.sql import functions as sf

In [2]: spark.range(1).select(sf.make_interval(sf.lit(1))).schema
Out[2]: StructType([StructField('make_interval(1, 0, 0, 0, 0, 0, 0)', CalendarIntervalType(), True)])
```

### How was this patch tested?
added UT

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

Closes apache#42743 from zhengruifeng/py_connect_cal_interval.

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

python/pyspark/sql/connect/types.py

@@ -33,6 +33,7 @@
     TimestampNTZType,
     DayTimeIntervalType,
     YearMonthIntervalType,
+    CalendarIntervalType,
     MapType,
     StringType,
     CharType,
@@ -169,6 +170,8 @@ def pyspark_types_to_proto_types(data_type: DataType) -> pb2.DataType:
     elif isinstance(data_type, YearMonthIntervalType):
         ret.year_month_interval.start_field = data_type.startField
         ret.year_month_interval.end_field = data_type.endField
+    elif isinstance(data_type, CalendarIntervalType):
+        ret.calendar_interval.CopyFrom(pb2.DataType.CalendarInterval())
     elif isinstance(data_type, StructType):
         struct = pb2.DataType.Struct()
         for field in data_type.fields:
@@ -265,6 +268,8 @@ def proto_schema_to_pyspark_data_type(schema: pb2.DataType) -> DataType:
             else None
         )
         return YearMonthIntervalType(startField=start, endField=end)
+    elif schema.HasField("calendar_interval"):
+        return CalendarIntervalType()
     elif schema.HasField("array"):
         return ArrayType(
             proto_schema_to_pyspark_data_type(schema.array.element_type),

python/pyspark/sql/tests/connect/test_parity_types.py

@@ -86,7 +86,7 @@ def test_rdd_with_udt(self):
     def test_udt(self):
         super().test_udt()
 
-    @unittest.skip("SPARK-45018: should support CalendarIntervalType")
+    @unittest.skip("SPARK-45026: spark.sql should support datatypes not compatible with arrow")
     def test_calendar_interval_type(self):
         super().test_calendar_interval_type()
 

python/pyspark/sql/tests/test_types.py

@@ -1284,6 +1284,10 @@ def test_calendar_interval_type(self):
         schema1 = self.spark.sql("SELECT make_interval(100, 11, 1, 1, 12, 30, 01.001001)").schema
         self.assertEqual(schema1.fields[0].dataType, CalendarIntervalType())
 
+    def test_calendar_interval_type_with_sf(self):
+        schema1 = self.spark.range(1).select(F.make_interval(F.lit(1))).schema
+        self.assertEqual(schema1.fields[0].dataType, CalendarIntervalType())
+
 
 class DataTypeTests(unittest.TestCase):
     # regression test for SPARK-6055