**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`.


tests/jobs/test_scheduler_job.py
session.add(dag_model)
        dr1 = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=DEFAULT_DATE,
            state=State.RUNNING,
        )
        dr2 = dag.create_dagrun(
            run_type=DagRunType.BACKFILL_JOB,
            execution_date=dag.following_schedule(dr1.execution_date),
            state=State.RUNNING,
        )
        ti_no_dagrun = TaskInstance(task1, DEFAULT_DATE - datetime.timedelta(days=1))
        ti_backfill = TaskInstance(task1, dr2.execution_date)
        ti_with_dagrun = TaskInstance(task1, dr1.execution_date)
        
        ti_no_dagrun.state = State.SCHEDULED
        ti_backfill.state = State.SCHEDULED
        ti_with_dagrun.state = State.SCHEDULED
        session.merge(dr2)
        session.merge(ti_no_dagrun)
        session.merge(ti_backfill)
        session.merge(ti_with_dagrun)
        session.flush()
        res = self.scheduler_job._executable_task_instances_to_queued(max_tis=32, session=session)
        assert 2 == len(res)
        res_keys = map(lambda x: x.key, res)
        assert ti_no_dagrun.key in res_keys
        assert ti_with_dagrun.key in res_keys
        session.rollback()
    def test_find_executable_task_instances_pool(self):