Outer join filter pushdown in null supplying table when condition is on one of the joined columns

df1 = spark.createDataFrame([{ "a": 1, "b" : 2}, { "a": 3, "b" : 4}])
df2 = spark.createDataFrame([{ "a": 1, "c" : 5}, { "a": 3, "c" : 6}, { "a": 5, "c" : 8}])

df1.join(df2, ['a'], 'right_outer').where("b = 2").explain()

== Physical Plan ==
*Project [a#16299L, b#16295L, c#16300L]
+- *SortMergeJoin [a#16294L], [a#16299L], Inner
   :- *Sort [a#16294L ASC NULLS FIRST], false, 0
   :  +- Exchange hashpartitioning(a#16294L, 4)
   :     +- *Filter ((isnotnull(b#16295L) && (b#16295L = 2)) && isnotnull(a#16294L))
   :        +- Scan ExistingRDD[a#16294L,b#16295L]
   +- *Sort [a#16299L ASC NULLS FIRST], false, 0
      +- Exchange hashpartitioning(a#16299L, 4)
         +- *Filter isnotnull(a#16299L)
            +- Scan ExistingRDD[a#16299L,c#16300L]


df1 = spark.createDataFrame([{ "a": 1, "b" : 2}, { "a": 3, "b" : 4}])
df2 = spark.createDataFrame([{ "a": 1, "c" : 5}, { "a": 3, "c" : 6}, { "a": 5, "c" : 8}])

df1.join(df2, ['a'], 'right_outer').where("a = 1").explain()

== Physical Plan ==
*Project [a#16314L, b#16310L, c#16315L]
+- SortMergeJoin [a#16309L], [a#16314L], RightOuter
   :- *Sort [a#16309L ASC NULLS FIRST], false, 0
   :  +- Exchange hashpartitioning(a#16309L, 4)
   :     +- Scan ExistingRDD[a#16309L,b#16310L]
   +- *Sort [a#16314L ASC NULLS FIRST], false, 0
      +- Exchange hashpartitioning(a#16314L, 4)
         +- *Filter (isnotnull(a#16314L) && (a#16314L = 1))
            +- Scan ExistingRDD[a#16314L,c#16315L]