Fix warning condition in MLLib RankingMetrics ndcgAk

The implementation of nDCG evaluation in MLLib with relevance score (added in 3.4.0, see https://issues.apache.org/jira/browse/SPARK-39446 and pull request) implements the following warning when the input data isn't correct: "# of ground truth set and # of relevance value set should be equal, check input data"

The logic for raising warnings is faulty at the moment: it raises a warning when the following conditions are both true:

1. rel is empty
2. lab.size and rel.size are not equal.

With the current logic, RankingMetrics will:

• raise incorrect warning when a user is using it in the "binary" mode (i.e. no relevance values in the input)
• not raise warning (that could be necessary) when the user is using it in the "non-binary" model (i.e. with relevance values in the input)

The logic should be to raise a warning should be:

1. rel is not empty
2. lab.size and rel.size are not equal.