[SPARK-18035][SQL] Introduce performant and memory efficient APIs to create ArrayBasedMapData

## What changes were proposed in this pull request?

Jira: https://issues.apache.org/jira/browse/SPARK-18035

In HiveInspectors, I saw that converting Java map to Spark's `ArrayBasedMapData` spent quite sometime in buffer copying : https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala#L658

The reason being `map.toSeq` allocates a new buffer and copies the map entries to it: https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/MapLike.scala#L323

This copy is not needed as we get rid of it once we extract the key and value arrays.

Here is the call trace:

```
org.apache.spark.sql.hive.HiveInspectors$$anonfun$unwrapperFor$41.apply(HiveInspectors.scala:664)
scala.collection.AbstractMap.toSeq(Map.scala:59)
scala.collection.MapLike$class.toSeq(MapLike.scala:323)
scala.collection.AbstractMap.toBuffer(Map.scala:59)
scala.collection.MapLike$class.toBuffer(MapLike.scala:326)
scala.collection.AbstractTraversable.copyToBuffer(Traversable.scala:104)
scala.collection.TraversableOnce$class.copyToBuffer(TraversableOnce.scala:275)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
scala.collection.AbstractIterable.foreach(Iterable.scala:54)
scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
scala.collection.Iterator$class.foreach(Iterator.scala:893)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
```

Also, earlier code was populating keys and values arrays separately by iterating twice. The PR avoids double iteration of the map and does it in one iteration.

EDIT: During code review, there were several more places in the code which were found to do similar thing. The PR dedupes those instances and introduces convenient APIs which are performant and memory efficient

## Performance gains

The number is subjective and depends on how many map columns are accessed in the query and average entries per map. For one the queries that I tried out, I saw 3% CPU savings (end-to-end) for the query.

## How was this patch tested?

This does not change the end result produced so relying on existing tests.

Author: Tejas Patil <tejasp@fb.com>

Closes #15573 from tejasapatil/SPARK-18035_avoid_toSeq.