[SPARK-15743][SQL] Prevent saving with all-column partitioning

## What changes were proposed in this pull request?

When saving datasets on storage, `partitionBy` provides an easy way to construct the directory structure. However, if a user choose all columns as partition columns, some exceptions occurs.

- **ORC with all column partitioning**: `AnalysisException` on **future read** due to schema inference failure.
 ```scala
scala> spark.range(10).write.format("orc").mode("overwrite").partitionBy("id").save("/tmp/data")

scala> spark.read.format("orc").load("/tmp/data").collect()
org.apache.spark.sql.AnalysisException: Unable to infer schema for ORC at /tmp/data. It must be specified manually;
```

- **Parquet with all-column partitioning**: `InvalidSchemaException` on **write execution** due to Parquet limitation.
 ```scala
scala> spark.range(100).write.format("parquet").mode("overwrite").partitionBy("id").save("/tmp/data")
[Stage 0:>                                                          (0 + 8) / 8]16/06/02 16:51:17
ERROR Utils: Aborting task
org.apache.parquet.schema.InvalidSchemaException: A group type can not be empty. Parquet does not support empty group without leaves. Empty group: spark_schema
... (lots of error messages)
```

Although some formats like JSON support all-column partitioning without any problem, it seems not a good idea to make lots of empty directories.

This PR prevents saving with all-column partitioning by consistently raising `AnalysisException` before executing save operation.

## How was this patch tested?

Newly added `PartitioningUtilsSuite`.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13486 from dongjoon-hyun/SPARK-15743.

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