[SPARK-3573][MLLIB] Make MLlib's Vector compatible with SQL's SchemaRDD

Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley.

~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~

marmbrus jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #3070 from mengxr/SPARK-3573 and squashes the following commits:

3a0b6e5 [Xiangrui Meng] organize imports
236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
[SPARK-4163][Core] Add a backward compatibility test for FetchFailed

/cc aarondav

Author: zsxwing <zsxwing@gmail.com>

Closes #3086 from zsxwing/SPARK-4163-back-comp and squashes the following commits:

21cb2a8 [zsxwing] Add a backward compatibility test for FetchFailed
[SPARK-4166][Core] Add a backward compatibility test for ExecutorLostFailure

Author: zsxwing <zsxwing@gmail.com>

Closes #3085 from zsxwing/SPARK-4166-back-comp and squashes the following commits:

89329f4 [zsxwing] Add a backward compatibility test for ExecutorLostFailure