[SPARK-16907][SQL] Fix performance regression for parquet table when vectorized parquet record reader is not being used

## What changes were proposed in this pull request?

For non-partitioned parquet table, if the vectorized parquet record reader is not being used, Spark 2.0 adds an extra unnecessary memory copy to append partition values for each row.

There are several typical cases that vectorized parquet record reader is not being used:
1. When the table schema is not flat, like containing nested fields.
2. When `spark.sql.parquet.enableVectorizedReader = false`

By fixing this bug, we get about 20% - 30% performance gain in test case like this:

```
// Generates parquet table with nested columns
spark.range(100000000).select(struct($"id").as("nc")).write.parquet("/tmp/data4")

def time[R](block: => R): Long = {
    val t0 = System.nanoTime()
    val result = block    // call-by-name
    val t1 = System.nanoTime()
    println("Elapsed time: " + (t1 - t0)/1000000 + "ms")
    (t1 - t0)/1000000
}

val x = ((0 until 20).toList.map(x => time(spark.read.parquet("/tmp/data4").filter($"nc.id" < 100).collect()))).sum/20
```

## How was this patch tested?

After a few times warm up, we get 26% performance improvement

Before fix:
```
Average: 4584ms, raw data (10 tries): 4726ms 4509ms 4454ms 4879ms 4586ms 4733ms 4500ms 4361ms 4456ms 4640ms
```

After fix:
```
Average: 3614ms, raw data(10 tries): 3554ms 3740ms 4019ms 3439ms 3460ms 3664ms 3557ms 3584ms 3612ms 3531ms
```

Test env: Intel(R) Core(TM) i7-6700 CPU  3.40GHz, Intel SSD SC2KW24

Author: Sean Zhong <seanzhong@databricks.com>

Closes #14445 from clockfly/fix_parquet_regression_2.

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