**Apache Airflow version**: 2.0.0a2

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.15.12

**Environment**:
- **OS** (e.g. from /etc/os-release): CentOS 7


**What happened**:

When using KubernetesExecutor, a single SIGTERM doesn't result in the scheduler actually exiting cleanly. However, it will exit after receiving a second SIGTERM.

The same behavior happens if you have an exception in the scheduler. An easy way to replicate it is to raise in `pod_mutation_hook`.

In practice, this means the scheduler doesn't get restarted by K8s after encountering a exception. Or, when terminating the scheduler pod, it sticks around until k8s ultimately sends it a SIGKILL after its grace period is up.

**What you expected to happen**:

The scheduler to exit after a single SIGTERM.

**How to reproduce it**:
The easiest way to reproduce it is to simply send a SIGKILL to your KubernetesExecutor scheduler pod:

```
$ kubectl exec {scheduler_pod_name} kill 1
```

Note, the pod stays running, while the logs report:

```
[2020-10-30 17:34:09,581] {scheduler_job.py:794} INFO - Exiting gracefully upon receiving signal 15
[2020-10-30 17:34:10,586] {process_utils.py:97} INFO - Sending Signals.SIGTERM to GPID 59
[2020-10-30 17:34:10,879] {process_utils.py:63} INFO - Process psutil.Process(pid=59, status='terminated', exitcode=0, started=
'17:33:23') (59) terminated with exit code 0
[2020-10-30 17:34:10,882] {process_utils.py:97} INFO - Sending Signals.SIGTERM to GPID 59
[2020-10-30 17:34:10,882] {scheduler_job.py:1329} INFO - Exited execute loop
```

Issue another `kill`:
```
$ kubectl exec {scheduler_pod_name} kill 1
```

And the pod will be restarted by k8s.

If you can't reproduce it, let me know and I'll try and get something running on kind.

**Anything else we need to know**:

The same behavior happens if you have an exception in the scheduler. An easy way to replicate it is to raise in a `pod_mutation_hook`.


airflow/executors/local_executor.py
"""
    LocalExecutor executes tasks locally in parallel.
    It uses the multiprocessing Python library and queues to parallelize the execution
    of tasks.
    :param parallelism: how many parallel processes are run in the executor
    """
    def __init__(self, parallelism: int = PARALLELISM):
        super().__init__(parallelism=parallelism)
        self.manager: Optional[SyncManager] = None
        self.result_queue: Optional['Queue[TaskInstanceStateType]'] = None
        self.workers: List[QueuedLocalWorker] = []
        self.workers_used: int = 0
        self.workers_active: int = 0
        self.impl: Optional[
            Union['LocalExecutor.UnlimitedParallelism', 'LocalExecutor.LimitedParallelism']
        ] = None
    class UnlimitedParallelism:
        """
        Implements LocalExecutor with unlimited parallelism, starting one process
        per each command to execute.
        :param executor: the executor instance to implement.
        """
        def __init__(self, executor: 'LocalExecutor'):