[SPARK-16896][SQL] Handle duplicated field names in header consistently with...

[SPARK-16896][SQL] Handle duplicated field names in header consistently with null or empty strings in CSV

## What changes were proposed in this pull request?

Currently, CSV datasource allows to load duplicated empty string fields or fields having `nullValue` in the header. It'd be great if this can deal with normal fields as well.

This PR proposes handling the duplicates consistently with the existing behaviour with considering case-sensitivity (`spark.sql.caseSensitive`) as below:

data below:

```
fieldA,fieldB,,FIELDA,fielda,,
1,2,3,4,5,6,7
```

is parsed as below:

```scala
spark.read.format("csv").option("header", "true").load("test.csv").show()
```

- when `spark.sql.caseSensitive` is `false` (by default).

  ```
  +-------+------+---+-------+-------+---+---+
  |fieldA0|fieldB|_c2|FIELDA3|fieldA4|_c5|_c6|
  +-------+------+---+-------+-------+---+---+
  |      1|     2|  3|      4|      5|  6|  7|
  +-------+------+---+-------+-------+---+---+
  ```

- when `spark.sql.caseSensitive` is `true`.

  ```
  +-------+------+---+-------+-------+---+---+
  |fieldA0|fieldB|_c2| FIELDA|fieldA4|_c5|_c6|
  +-------+------+---+-------+-------+---+---+
  |      1|     2|  3|      4|      5|  6|  7|
  +-------+------+---+-------+-------+---+---+
  ```

**In more details**,

There is a good reference about this problem, `read.csv()` in R. So, I initially wanted to propose the similar behaviour.

In case of R,  the CSV data below:

```
fieldA,fieldB,,fieldA,fieldA,,
1,2,3,4,5,6,7
```

is parsed as below:

```r
test <- read.csv(file="test.csv",header=TRUE,sep=",")
> test
  fieldA fieldB X fieldA.1 fieldA.2 X.1 X.2
1      1      2 3        4        5   6   7
```

However, Spark CSV datasource already is handling duplicated empty strings and `nullValue` as field names. So the data below:

```
,,,fieldA,,fieldB,
1,2,3,4,5,6,7
```

is parsed as below:

```scala
spark.read.format("csv").option("header", "true").load("test.csv").show()
```
```
+---+---+---+------+---+------+---+
|_c0|_c1|_c2|fieldA|_c4|fieldB|_c6|
+---+---+---+------+---+------+---+
|  1|  2|  3|     4|  5|     6|  7|
+---+---+---+------+---+------+---+
```

R starts the number for each duplicate but Spark adds the number for its position for all fields for `nullValue` and empty strings.

In terms of case-sensitivity, it seems R is case-sensitive as below: (it seems it is not configurable).

```
a,a,a,A,A
1,2,3,4,5
```

is parsed as below:

```r
test <- read.csv(file="test.csv",header=TRUE,sep=",")
> test
  a a.1 a.2 A A.1
1 1   2   3 4   5
```

## How was this patch tested?

Unit test in `CSVSuite`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14745 from HyukjinKwon/SPARK-16896.