**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
assert ti2.state == State.QUEUED
        ti2.state = None
        session.flush()
        
        manual_run = dag.create_dagrun(
            run_type=DagRunType.MANUAL,
            execution_date=DEFAULT_DATE + timedelta(hours=1),
            state=State.RUNNING,
            session=session,
        )
        session.flush()
        num_queued = self.scheduler_job._do_scheduling(session)
        assert num_queued == 1
        
        ti2 = dag_run.get_task_instance(task2.task_id, session)
        assert ti2.state == State.QUEUED
        
        ti1 = manual_run.get_task_instance(task1.task_id, session)
        assert ti1.state is None
@pytest.mark.xfail(reason="Work out where this goes")
def test_task_with_upstream_skip_process_task_instances():
    """
    Test if _process_task_instances puts a task instance into SKIPPED state if any of its
    upstream tasks are skipped according to TriggerRuleDep.
    """
    clear_db_runs()
    with DAG(
        dag_id='test_task_with_upstream_skip_dag', start_date=DEFAULT_DATE, schedule_interval=None
    ) as dag:
        dummy1 = DummyOperator(task_id='dummy1')