**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/jobs/scheduler_job.py
return executable_tis
        
        task_instance_str = "\n\t".join([repr(x) for x in task_instances_to_examine])
        self.log.info("%s tasks up for execution:\n\t%s", len(task_instances_to_examine), task_instance_str)
        pool_to_task_instances: DefaultDict[str, List[models.Pool]] = defaultdict(list)
        for task_instance in task_instances_to_examine:
            pool_to_task_instances[task_instance.pool].append(task_instance)
        
        dag_concurrency_map: DefaultDict[str, int]
        task_concurrency_map: DefaultDict[Tuple[str, str], int]
        dag_concurrency_map, task_concurrency_map = self.__get_concurrency_maps(
            states=list(EXECUTION_STATES), session=session
        )
        num_tasks_in_executor = 0
        
        num_starving_tasks_total = 0
        
        
        
        for pool, task_instances in pool_to_task_instances.items():
            pool_name = pool
            if pool not in pools:
                self.log.warning("Tasks using non-existent pool '%s' will not be scheduled", pool)
                continue
            open_slots = pools[pool]["open"]
            num_ready = len(task_instances)
            self.log.info(
                "Figuring out tasks to run in Pool(name=%s) with %s open slots "
                "and %s task instances ready to be queued",
                pool,