Overflow in Parquet row group filter creation causes incorrect results

While the parquet readers don't support reading parquet values into larger Spark types, it's possible to trigger an overflow when creating a Parquet row group filter that will then incorrectly skip row groups and bypass the exception in the reader,

Repro:

Seq(0).toDF("a").write.parquet(path)
spark.read.schema("a LONG").parquet(path).where(s"a < ${Long.MaxValue}").collect()

This succeeds and returns no results. This should either fail if the Parquet reader doesn't support the upcast from int to long or produce result `[0]` if it does.