### What changes were proposed in this pull request?
Introduces `InternalField` to manage dtypes and `StructField`s.
`InternalFrame` is already managing dtypes, but when it checks the Spark's data types, column names, and nullabilities, it tries to run the analysis phase each time it needs, which will cause a performance issue.
It will use `InternalField` class which stores the retrieved Spark's data types, column names, and nullabilities, and reuse them. Also, in case those can be known, just update and reuse them without asking Spark.
### Why are the changes needed?
Currently there are some performance issues in the pandas-on-Spark layer.
One of them is accessing Java DataFrame and run analysis phase too many times, especially just for retrieving the current column names or data types.
We should reduce the amount of unnecessary access.
### Does this PR introduce _any_ user-facing change?
Improves the performance in pandas-on-Spark layer:
```py
df = ps.read_parquet("/path/to/test.parquet") # contains ~75 columns
df = df[(df["col"] > 0) & (df["col"] < 10000)]
```
Before the PR, it took about **2.15 sec** and after **1.15 sec**.
### How was this patch tested?
Existing tests.
Closes#32775 from ueshin/issues/SPARK-35638/field.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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