[SPARK-3477] Clean up code in Yarn Client / ClientBase

This is part of a broader effort to clean up the Yarn integration code after #2020.

The high-level changes in this PR include:
- Removing duplicate code, especially across the alpha and stable APIs
- Simplify unnecessarily complex method signatures and hierarchies
- Rename unclear variable and method names
- Organize logging output produced when the user runs Spark on Yarn
- Extensively add documentation
- Privatize classes where possible

I have tested the stable API on a Hadoop 2.4 cluster. I tested submitting a jar that references classes in other jars in both client and cluster mode. I also made changes in the alpha API, though I do not have access to an alpha cluster. I have verified that it compiles, but it would be ideal if others can help test it.

For those interested in some examples in detail, please read on.

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***Appendix***

- The loop to `getApplicationReport` from the RM is duplicated in 4 places: in the stable `Client`, alpha `Client`, and twice in `YarnClientSchedulerBackend`. We should not have different loops for client and cluster deploy modes.
- There are many fragmented small helper methods that are only used once and should just be inlined. For instance, `ClientBase#getLocalPath` returns `null` on certain conditions, and its only caller `ClientBase#addFileToClasspath` checks whether the value returned is `null`. We could just have the caller check on that same condition to avoid passing `null`s around.
- In `YarnSparkHadoopUtil#addToEnvironment`, we take in an argument `classpathSeparator` that always has the same value upstream (i.e. `File.separator`, which by the way is simply wrong; it should be `File.pathSeparator`). This argument is now removed from the signature and all callers of this method upstream.
- `ClientBase#copyRemoteFile` is now renamed to `copyFileToRemote`. It was unclear whether we are copying a remote file to our local file system, or copying a locally visible file to a remote file system. Also, even the content of the method has inaccurately named variables. We use `val remoteFs` to signify the file system of the locally visible file and `val fs` to signify the remote, destination file system. These are now renamed `srcFs` and `destFs` respectively.
- We currently log the AM container's environment and resource mappings directly as Scala collections. This is incredibly hard to read and probably too verbose for the average Spark user. In other modes (e.g. standalone), we also don't log the launch commands by default, so the logging level of these information is now set to `DEBUG`.
- None of these classes (`Client`, `ClientBase`, `YarnSparkHadoopUtil` etc.) is intended to be used by a Spark application (the user should go through Spark submit instead). At the very least they should be `private[spark]`.
That's because ExecutorLauncher is an object, not a class.

You can use `ExecutorLauncher.getClass.getName.stripSuffix("$")` if you think it looks better.