[SPARK-2010] [PySpark] [SQL] support nested structure in SchemaRDD

Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes.

This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance.

root
 |-- field1: integer (nullable = true)
 |-- field2: string (nullable = true)
 |-- field3: struct (nullable = true)
 |    |-- field4: integer (nullable = true)
 |    |-- field5: array (nullable = true)
 |    |    |-- element: integer (containsNull = false)
 |-- field6: array (nullable = true)
 |    |-- element: struct (containsNull = false)
 |    |    |-- field7: string (nullable = true)

Then we can access them by row.field3.field5[0]  or row.field6[5].field7

It also will infer the schema in Python, convert dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType.

PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable.
Does this behave properly in the unbatched case?  In general, I don't think that it's safe to detect batching by checking whether the returned item is a list, since that might inadvertently flatten a list-of-lists that was serialized without batching.

(see https://github.com/apache/spark/pull/1338#discussion-diff-15508664)