Eliminates these repeated computation in multi aggregations query

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https://github.com/pingcap/tispark/commit/dcca23bfa1aa0c356a4280d82bc8301c0de08318

scala> spark.sql ("select count(number),avg(number) from person").explain

== Physical Plan ==

*HashAggregate(keys=[], functions=[sum(count(number#0L)#42L), sum(sum(number#0L)#43L), sum(count(number#0L)#45L)])

+- Exchange SinglePartition

   +- *HashAggregate(keys=[], functions=[partial_sum(count(number#0L)#42L), partial_sum(sum(number#0L)#43L), partial_sum(count(number#0L)#45L)])

      +- Scan CoprocessorRDD[count(number#0L)#42L,sum(number#0L)#43L,count(number#0L)#45L]

scala> spark.sql ("select count(number),avg(number) from person").explain()

== Physical Plan ==

*HashAggregate(keys=[], functions=[sum(count(number#0L)#20L), sum(sum(number#0L)#21L), sum(count(number#0L)#20L)])

+- Exchange SinglePartition

   +- *HashAggregate(keys=[], functions=[partial_sum(count(number#0L)#20L), partial_sum(sum(number#0L)#21L), partial_sum(count(number#0L)#20L)])

      +- Scan CoprocessorRDD[count(number#0L)#20L,sum(number#0L)#21L]