[SPARK-17817] [PYSPARK] [FOLLOWUP] PySpark RDD Repartitioning Results in...

[SPARK-17817] [PYSPARK] [FOLLOWUP] PySpark RDD Repartitioning Results in Highly Skewed Partition Sizes

## What changes were proposed in this pull request?

This change is a followup for #15389 which calls `_to_java_object_rdd()` to solve this issue. Due to the concern of the possible expensive cost of the call, we can choose to decrease the batch size to solve this issue too.

Simple benchmark:

    import time
    num_partitions = 20000
    a = sc.parallelize(range(int(1e6)), 2)
    start = time.time()
    l = a.repartition(num_partitions).glom().map(len).collect()
    end = time.time()
    print(end - start)

Before: 419.447577953
_to_java_object_rdd(): 421.916361094
decreasing the batch size: 423.712255955

## How was this patch tested?

Jenkins tests.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #15445 from viirya/repartition-batch-size.