[WIP][SPARK-3795] Heuristics for dynamically scaling executors

This is part of a bigger effort to provide elastic scaling of executors within a Spark application ([SPARK-3174](https://issues.apache.org/jira/browse/SPARK-3174)). This PR does not provide any functionality by itself; it is a skeleton that is missing a mechanism to be added later in [SPARK-3822](https://issues.apache.org/jira/browse/SPARK-3822).

The design doc can be found at [SPARK-3174](https://issues.apache.org/jira/browse/SPARK-3174) (under "Heuristics for Scaling Executors". This deviates from the design in two ways: (1) the remove policy is significantly simplified by cutting out the exponential increase logic that mirrors the add policy, and (2) the configuration of the add policy is split up into `addExecutorThreshold` and `addExecutorInterval`. The main reason for this is that the user may want to configure how quickly executors are added, but want to keep the trigger condition the same.

Comments and feedback are most welcome, but please keep in mind that this is still WIP! For those of you reviewing this in detail, I highly recommend doing it through your favorite IDE instead of through the diff here.
Good question. I spent a long time perusing whether we could just use `allPendingTasks`. Apparently we remove pending tasks there lazily (when we call `findTaskFromList`) instead of as soon as it's been scheduled. In most cases, I've noticed that this is actually usually emptied all at once because we usually grab the task from other lists with more specific locality preferences. This means whatever's in there is actually not representative of what's actually still pending to be scheduled, so we can't use it.