[SPARK-2871] [PySpark] add zipWithIndex() and zipWithUniqueId()

RDD.zipWithIndex()

        Zips this RDD with its element indices.

        The ordering is first based on the partition index and then the
        ordering of items within each partition. So the first item in
        the first partition gets index 0, and the last item in the last
        partition receives the largest index.

        This method needs to trigger a spark job when this RDD contains
        more than one partitions.

        >>> sc.parallelize(range(4), 2).zipWithIndex().collect()
        [(0, 0), (1, 1), (2, 2), (3, 3)]


RDD.zipWithUniqueId()

        Zips this RDD with generated unique Long ids.

        Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
        n is the number of partitions. So there may exist gaps, but this
        method won't trigger a spark job, which is different from
        L{zipWithIndex}

        >>> sc.parallelize(range(4), 2).zipWithUniqueId().collect()
        [(0, 0), (2, 1), (1, 2), (3, 3)]
This isn't the best example because it's not clear which element is the item and which element is its index.  In the Scala API, this is clear from the method's return type.  Maybe we should update the documentation to explicitly state that the second element is the id (like the Scala API).

I think this implementation has things backwards w.r.t. the Scala one:

```python
>>> sc.parallelize(['a', 'b', 'c', 'd'], 2).zipWithIndex().collect()
[(0, 'a'), (1, 'b'), (2, 'c'), (3, 'd')]
```

versus

```scala
scala> sc.parallelize(Seq('a', 'b', 'c', 'd')).zipWithIndex().collect()
res0: Array[(Char, Long)] = Array((a,0), (b,1), (c,2), (d,3))
```