## What changes were proposed in this pull request?
This PR is to fix a regression introduced in: https://github.com/apache/spark/pull/21004/files#r236998030
If user specifies schema, Spark don't need to infer data type for of partition columns, otherwise the data type might not match with the one user provided.
E.g. for partition directory `p=4d`, after data type inference the column value will be `4.0`.
See https://issues.apache.org/jira/browse/SPARK-26188 for more details.
Note that user specified schema **might not cover all the data columns**:
```
val schema = new StructType()
.add("id", StringType)
.add("ex", ArrayType(StringType))
val df = spark.read
.schema(schema)
.format("parquet")
.load(src.toString)
assert(df.schema.toList === List(
StructField("ex", ArrayType(StringType)),
StructField("part", IntegerType), // inferred partitionColumn dataType
StructField("id", StringType))) // used user provided partitionColumn dataType
```
For the missing columns in user specified schema, Spark still need to infer their data types if `partitionColumnTypeInferenceEnabled` is enabled.
To implement the partially inference, refactor `PartitioningUtils.parsePartitions` and pass the user specified schema as parameter to cast partition values.
## How was this patch tested?
Add unit test.
Closes#23165 from gengliangwang/fixFileIndex.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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