Support java.sql.Date and java.sql.Timestamp so they work just as in plain Spark datasets.

Given the following snippet:

    import frameless._
    import org.apache.spark.sql.catalyst.util.DateTimeUtils

    implicit val dateAsInt: Injection[java.sql.Date, Int] = Injection(DateTimeUtils.fromJavaDate, DateTimeUtils.toJavaDate)

    // create some df (typically read from an orc or parquet file)
    val today = new java.sql.Date(System.currentTimeMillis)
    val df = Seq((42, today)).toDF("i", "d")

    // and turn it into a TypedDataset
    case class P(i: Int, d: java.sql.Date)
    val ds = df.as[P]
    val tds = TypedDataset.create(ds)

in plain Dataset you can use:

ds.filter(ds("d") === today).show 

+---+----------+
|  i|         d|
+---+----------+
| 42|2017-11-10|
+---+----------+

but in TypedDataset this results in an AnalysisException

tds.filter(tds('d) === today).show().run
org.apache.spark.sql.AnalysisException: cannot resolve '(`d` = FramelessLit(2017-11-10))' due to data type mismatch: differing types in '(`d` = FramelessLit(2017-11-10))' (date and int).;;
'Filter (d#82 = FramelessLit(2017-11-10))
+- Project [_1#78 AS i#81, _2#79 AS d#82]
   +- LocalRelation [_1#78, _2#79]

Start by reproducing the reported TypedDataset filter failure with java.sql.Date, then inspect how TypedDataset handles literals and type conversions. Check the corresponding handling for java.sql.Timestamp as well. Done means both types can be compared in TypedDataset expressions like their plain Spark Dataset equivalents without a date/int mismatch.