[SPARK-3086] [SPARK-3043] [SPARK-3156] [mllib]  DecisionTree aggregation improvements

Summary:
1. Variable numBins for each feature [SPARK-3043]
2. Reduced data reshaping in aggregation [SPARK-3043]
3. Choose ordering for ordered categorical features adaptively [SPARK-3156]
4. Changed nodes to use 1-indexing [SPARK-3086]
5. Small clean-ups

Note: This PR looks bigger than it is since I moved several functions from inside findBestSplitsPerGroup to outside of it (to make it clear what was being serialized in the aggregation).

Speedups: This update helps most when many features use few bins but a few features use many bins.  Some example results on speedups with 2M examples, 3.5K features (15-worker EC2 cluster):
* Example where old code was reasonably efficient (1/2 continuous, 1/4 binary, 1/4 20-category): 164.813 --> 116.491 sec
* Example where old code wasted many bins (1/10 continuous, 81/100 binary, 9/100 20-category): 128.701 --> 39.334 sec

Details:

(1) Variable numBins for each feature [SPARK-3043]

DecisionTreeMetadata now computes a variable numBins for each feature.  It also tracks numSplits.

(2) Reduced data reshaping in aggregation [SPARK-3043]

Added DTStatsAggregator, a wrapper around the aggregate statistics array for easy but efficient indexing.
* Added ImpurityAggregator and ImpurityCalculator classes, to make DecisionTree code more oblivious to the type of impurity.
* Design note: I originally tried creating Impurity classes which stored data and storing the aggregates in an Array[Array[Array[Impurity]]].  However, this led to significant slowdowns, perhaps because of overhead in creating so many objects.

The aggregate statistics are never reshaped, and cumulative sums are computed in-place.

Updated the layout of aggregation functions.  The update simplifies things by (1) dividing features into ordered/unordered (instead of ordered/unordered/continuous) and (2) making use of the DTStatsAggregator for indexing.
For this update, the following functions were refactored:
* updateBinForOrderedFeature
* updateBinForUnorderedFeature
* binaryOrNotCategoricalBinSeqOp
* multiclassWithCategoricalBinSeqOp
* regressionBinSeqOp
The above 5 functions were replaced with:
* orderedBinSeqOp
* someUnorderedBinSeqOp

Other changes:
* calculateGainForSplit now treats all feature types the same way.
* Eliminated extractLeftRightNodeAggregates.

(3) Choose ordering for ordered categorical features adaptively [SPARK-3156]

Updated binsToBestSplit():
* This now computes cumulative sums of stats for ordered features.
* For ordered categorical features, it chooses an ordering for categories. (This uses to be done by findSplitsBins.)
* Uses iterators to shorten code and avoid building an Array[Array[InformationGainStats]].

Side effects:
* In findSplitsBins: A sample of the data is only taken for data with continuous features.  It is not needed for data with only categorical features.
* In findSplitsBins: splits and bins are no longer pre-computed for ordered categorical features since they are not needed.
* TreePoint binning is simpler for categorical features.

(4) Changed nodes to use 1-indexing [SPARK-3086]

Nodes used to be indexed from 0.  Now they are indexed from 1.
Node indexing functions are now collected in object Node (Node.scala).

(5) Small clean-ups

Eliminated functions extractNodeInfo() and extractInfoForLowerLevels() to reduce duplicate code.
Eliminated InvalidBinIndex since it is no longer used.

CC: @mengxr  @manishamde  Please let me know if you have thoughts on this—thanks!
This actually does match the parameter meaning.  The parameter is set when DecisionTreeMetadata is constructed by buildMetadata, and it is set to be the actual max, not the max possible/allowed number of bins.