**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
super().__init__(target=self.do_work)
        self.daemon: bool = True
        self.result_queue: 'Queue[TaskInstanceStateType]' = result_queue
    def run(self):
        
        settings.engine.pool.dispose()
        settings.engine.dispose()
        return super().run()
    def execute_work(self, key: TaskInstanceKey, command: CommandType) -> None:
        """
        Executes command received and stores result state in queue.
        :param key: the key to identify the task instance
        :param command: the command to execute
        """
        if key is None:
            return
        self.log.info("%s running %s", self.__class__.__name__, command)
        if settings.EXECUTE_TASKS_NEW_PYTHON_INTERPRETER:
            state = self._execute_work_in_subprocess(command)
        else:
            state = self._execute_work_in_fork(command)
        self.result_queue.put((key, state))
    def _execute_work_in_subprocess(self, command: CommandType) -> str:
        try:
            subprocess.check_call(command, close_fds=True)
            return State.SUCCESS
        except subprocess.CalledProcessError as e:
            self.log.error("Failed to execute task %s.", str(e))
            return State.FAILED
    def _execute_work_in_fork(self, command: CommandType) -> str: