DeduplicateRelations rule breaks plan when co-grouping the same DataFrame

val df = spark.range(3)

val left_grouped_df = df.groupBy("id").as[Long, Long]
val right_grouped_df = df.groupBy("id").as[Long, Long]

val cogroup_df = left_grouped_df.cogroup(right_grouped_df) {
  case (key, left, right) => left
}

cogroup_df.explain()

== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- SerializeFromObject [input[0, bigint, false] AS value#12L]
   +- CoGroup, id#0: bigint, id#0: bigint, id#0: bigint, [id#13L], [id#13L], [id#13L], [id#13L], obj#11: bigint
      :- !Sort [id#13L ASC NULLS FIRST], false, 0
      :  +- !Exchange hashpartitioning(id#13L, 200), ENSURE_REQUIREMENTS, [plan_id=16]
      :     +- Range (0, 3, step=1, splits=16)
      +- Sort [id#13L ASC NULLS FIRST], false, 0
         +- Exchange hashpartitioning(id#13L, 200), ENSURE_REQUIREMENTS, [plan_id=17]
            +- Range (0, 3, step=1, splits=16)

cogroup_df.show()

java.lang.IllegalStateException: Couldn't find id#13L in [id#0L]