spark.createDataFrame fails with pandas DataFrame including pandas.NA

In [5]: from pyspark.sql import SparkSession

In [6]: spark = SparkSession.builder.getOrCreate()

In [7]: spark.conf.set("spark.sql.execution.arrow.enabled", "true")

In [8]: import numpy as np
   ...: import pandas as pd

In [12]: pdf = pd.DataFrame(data=[{'a':1,'b':2}, {'a':3,'b':4,'c':5}], dtype=pd.Int64Dtype())

In [16]: pdf
Out[16]:
   a  b     c
0  1  2  <NA>
1  3  4     5

In [13]: sdf = spark.createDataFrame(pdf)
/Users/ariga/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py:714: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true; however, failed by the reason below:
  Did not pass numpy.dtype object
Attempting non-optimization as 'spark.sql.execution.arrow.fallback.enabled' is set to true.
  warnings.warn(msg)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-13-ad13ba53b87e> in <module>
----> 1 sdf = spark.createDataFrame(df2)

~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py in createDataFrame(self, data, schema, samplingRatio, verifySchema)
    746             rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
    747         else:
--> 748             rdd, schema = self._createFromLocal(map(prepare, data), schema)
    749         jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
    750         jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())

~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py in _createFromLocal(self, data, schema)
    414
    415         if schema is None or isinstance(schema, (list, tuple)):
--> 416             struct = self._inferSchemaFromList(data, names=schema)
    417             converter = _create_converter(struct)
    418             data = map(converter, data)

~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/session.py in _inferSchemaFromList(self, data, names)
    346             warnings.warn("inferring schema from dict is deprecated,"
    347                           "please use pyspark.sql.Row instead")
--> 348         schema = reduce(_merge_type, (_infer_schema(row, names) for row in data))
    349         if _has_nulltype(schema):
    350             raise ValueError("Some of types cannot be determined after inferring")

~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/types.py in _merge_type(a, b, name)
   1099         fields = [StructField(f.name, _merge_type(f.dataType, nfs.get(f.name, NullType()),
   1100                                                   name=new_name(f.name)))
-> 1101                   for f in a.fields]
   1102         names = set([f.name for f in fields])
   1103         for n in nfs:

~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/types.py in <listcomp>(.0)
   1099         fields = [StructField(f.name, _merge_type(f.dataType, nfs.get(f.name, NullType()),
   1100                                                   name=new_name(f.name)))
-> 1101                   for f in a.fields]
   1102         names = set([f.name for f in fields])
   1103         for n in nfs:

~/src/pytd/.venv/lib/python3.6/site-packages/pyspark/sql/types.py in _merge_type(a, b, name)
   1092     elif type(a) is not type(b):
   1093         # TODO: type cast (such as int -> long)
-> 1094         raise TypeError(new_msg("Can not merge type %s and %s" % (type(a), type(b))))
   1095
   1096     # same type

TypeError: field c: Can not merge type <class 'pyspark.sql.types.StructType'> and <class 'pyspark.sql.types.LongType'>

In [15]: pyspark.__version__
Out[15]: '2.4.5'