[SPARK-13758][STREAMING][CORE] enhance exception message to avoid misleading

We have a recoverable Spark streaming job with checkpoint enabled, it could be executed correctly at first time, but throw following exception when restarted and recovered from checkpoint.
```
org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.
 	at org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$sc(RDD.scala:87)
 	at org.apache.spark.rdd.RDD.withScope(RDD.scala:352)
 	at org.apache.spark.rdd.RDD.union(RDD.scala:565)
 	at org.apache.spark.streaming.Repo$$anonfun$createContext$1.apply(Repo.scala:23)
 	at org.apache.spark.streaming.Repo$$anonfun$createContext$1.apply(Repo.scala:19)
 	at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
```

According to exception, it shows I invoked transformations and actions in other transformations, but I did not. The real reason is that I used external RDD in DStream operation. External RDD data is not stored in checkpoint, so that during recovering, the initial value of _sc in this RDD is assigned to null and hit above exception. But you can find the error message is misleading, it indicates nothing about the real issue
Here is the code to reproduce it.

```scala
object Repo {

  def createContext(ip: String, port: Int, checkpointDirectory: String):StreamingContext = {

    println("Creating new context")
    val sparkConf = new SparkConf().setAppName("Repo").setMaster("local[2]")
    val ssc = new StreamingContext(sparkConf, Seconds(2))
    ssc.checkpoint(checkpointDirectory)

    var cached = ssc.sparkContext.parallelize(Seq("apple, banana"))

    val words = ssc.socketTextStream(ip, port).flatMap(_.split(" "))
    words.foreachRDD((rdd: RDD[String]) => {
      val res = rdd.map(word => (word, word.length)).collect()
      println("words: " + res.mkString(", "))

      cached = cached.union(rdd)
      cached.checkpoint()
      println("cached words: " + cached.collect.mkString(", "))
    })
    ssc
  }

  def main(args: Array[String]) {

    val ip = "localhost"
    val port = 9999
    val dir = "/home/maowei/tmp"

    val ssc = StreamingContext.getOrCreate(dir,
      () => {
        createContext(ip, port, dir)
      })
    ssc.start()
    ssc.awaitTermination()
  }
}
```

Author: mwws <wei.mao@intel.com>

Closes #11595 from mwws/SPARK-MissleadingLog.

core/src/main/scala/org/apache/spark/rdd/RDD.scala

@@ -85,10 +85,14 @@ abstract class RDD[T: ClassTag](
   private def sc: SparkContext = {
     if (_sc == null) {
       throw new SparkException(
-        "RDD transformations and actions can only be invoked by the driver, not inside of other " +
-        "transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because " +
-        "the values transformation and count action cannot be performed inside of the rdd1.map " +
-        "transformation. For more information, see SPARK-5063.")
+        "This RDD lacks a SparkContext. It could happen in the following cases: \n(1) RDD " +
+        "transformations and actions are NOT invoked by the driver, but inside of other " +
+        "transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid " +
+        "because the values transformation and count action cannot be performed inside of the " +
+        "rdd1.map transformation. For more information, see SPARK-5063.\n(2) When a Spark " +
+        "Streaming job recovers from checkpoint, this exception will be hit if a reference to " +
+        "an RDD not defined by the streaming job is used in DStream operations. For more " +
+        "information, See SPARK-13758.")
     }
     _sc
   }