[SPARK-11840][SQL] Restore the 1.5's behavior of planning a single distinct aggregation.

The impact of this change is for a query that has a single distinct column and does not have any grouping expression like
`SELECT COUNT(DISTINCT a) FROM table`
The plan will be changed from
```
AGG-2 (count distinct)
  Shuffle to a single reducer
    Partial-AGG-2 (count distinct)
      AGG-1 (grouping on a)
        Shuffle by a
          Partial-AGG-1 (grouping on 1)
```
to the following one (1.5 uses this)
```
AGG-2
  AGG-1 (grouping on a)
    Shuffle to a single reducer
      Partial-AGG-1(grouping on a)
```
The first plan is more robust. However, to better benchmark the impact of this change, we should use 1.5's plan and use the conf of `spark.sql.specializeSingleDistinctAggPlanning` to control the plan.

Author: Yin Huai <yhuai@databricks.com>

Closes #9828 from yhuai/distinctRewriter.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DistinctAggregationRewriter.scala

@@ -126,8 +126,8 @@ case class DistinctAggregationRewriter(conf: CatalystConf) extends Rule[LogicalP
     val shouldRewrite = if (conf.specializeSingleDistinctAggPlanning) {
       // When the flag is set to specialize single distinct agg planning,
       // we will rely on our Aggregation strategy to handle queries with a single
-      // distinct column and this aggregate operator does have grouping expressions.
-      distinctAggGroups.size > 1 || (distinctAggGroups.size == 1 && a.groupingExpressions.isEmpty)
+      // distinct column.
+      distinctAggGroups.size > 1
     } else {
       distinctAggGroups.size >= 1
     }