[SPARK-3366][MLLIB]Compute best splits distributively in decision tree

Currently, all best splits are computed on the driver, which makes the driver a bottleneck for both communication and computation. This PR fix this problem by computed best splits on executors.
Instead of send all aggregate stats to the driver node, we can send aggregate stats for a node to a particular executor, using `reduceByKey` operation, then we can compute best split for this node there.

Implementation details:

Each node now has a nodeStatsAggregator, which save aggregate stats for all features and bins.
First use mapPartition to compute node aggregate stats for all nodes in each partition.
Then transform node aggregate stats to (nodeIndex, nodeStatsAggregator) pairs and use to `reduceByKey` operation to combine nodeStatsAggregator for the same node.
After all stats have been combined, best splits can be computed for each node based on the node aggregate stats. Best split result is collected to driver to construct the decision tree.

CC: mengxr manishamde jkbradley, please help me review this, thanks.
Error in doc (not code):
The left & right child stats are actually in:
[0, numBins(featureIndex) / 2) and
[numBins(featureIndex) / 2, numBins(featureIndex))
The semantics of "numBins" was updated in a previous PR so that 1 "bin" holds the same number of values for any type of feature.  (You can see the update in DecisionTreeMetadata.)