**Apache Airflow version**: 1.10.13 onwards (Any version that picked up #11278, including Airflow 2.0.* and 2.1.*)


**Environment**:
- **Cloud provider or hardware configuration**: Any
- **OS** (e.g. from /etc/os-release): Only tested on Debian Linux, but others may be affected too
- **Kernel** (e.g. `uname -a`): Any
- **Install tools**: Any
- **Others**: Only celery_executor is affected

**What happened**:
This was first reported [here](https://github.com/apache/airflow/issues/7935#issuecomment-839656436).
airflow-scheduler sometimes stops heartbeating and stops scheduling any tasks with this last line in the log. This happen at random times, a few times a week. Happens more often if the scheduler machine is slow. 

```
{scheduler_job.py:746} INFO - Exiting gracefully upon receiving signal 15
```

The problem is that sometimes the machine is slow, `reset_signals()` of one or more slow child processes is not yet called before other child processes send `SIGTERM` when they exit. As a result, the slow child processes respond to the `SIGTERM` as if they are the main scheduler process. Thus we see the `Exiting gracefully upon receiving signal 15` in the scheduler log. Since the probability of this happening is very low, this issue is really difficult to reproduce reliably in production.

Related to #7935
Most likely caused by #11278

**What you expected to happen**: 
Scheduler should not become stuck

**How to reproduce it**:

Here's a small reproducing example of the problem. There's roughly 1/25 chance it will be stuck. Run it many times to see it happen.

```python
#!/usr/bin/env python3.8
import os
import random
import signal
import time
from multiprocessing import Pool


def send_task_to_executor(arg):
    pass


def _exit_gracefully(signum, frame):
    print(f"{os.getpid()} Exiting gracefully upon receiving signal {signum}")


def register_signals():
    print(f"{os.getpid()} register_signals()")
    signal.signal(signal.SIGINT, _exit_gracefully)
    signal.signal(signal.SIGTERM, _exit_gracefully)
    signal.signal(signal.SIGUSR2, _exit_gracefully)


def reset_signals():
    if random.randint(0, 500) == 0:
        # This sleep statement here simulates the machine being busy
        print(f"{os.getpid()} is slow")
        time.sleep(0.1)
    signal.signal(signal.SIGINT, signal.SIG_DFL)
    signal.signal(signal.SIGTERM, signal.SIG_DFL)
    signal.signal(signal.SIGUSR2, signal.SIG_DFL)


if __name__ == "__main__":
    register_signals()

    task_tuples_to_send = list(range(20))
    sync_parallelism = 15
    chunksize = 5

    with Pool(processes=sync_parallelism, initializer=reset_signals) as pool:
        pool.map(
            send_task_to_executor,
            task_tuples_to_send,
            chunksize=chunksize,
        )


```

The reproducing example above can become stuck with a `py-spy dump` that looks exactly like what airflow scheduler does:

`py-spy dump` for the parent `airflow scheduler` process
```
Python v3.8.7

Thread 0x7FB54794E740 (active): "MainThread"
    poll (multiprocessing/popen_fork.py:27)
    wait (multiprocessing/popen_fork.py:47)
    join (multiprocessing/process.py:149)
    _terminate_pool (multiprocessing/pool.py:729)
    __call__ (multiprocessing/util.py:224)
    terminate (multiprocessing/pool.py:654)
    __exit__ (multiprocessing/pool.py:736)
    _send_tasks_to_celery (airflow/executors/celery_executor.py:331)
    _process_tasks (airflow/executors/celery_executor.py:272)
    trigger_tasks (airflow/executors/celery_executor.py:263)
    heartbeat (airflow/executors/base_executor.py:158)
    _run_scheduler_loop (airflow/jobs/scheduler_job.py:1388)
    _execute (airflow/jobs/scheduler_job.py:1284)
    run (airflow/jobs/base_job.py:237)
    scheduler (airflow/cli/commands/scheduler_command.py:63)
    wrapper (airflow/utils/cli.py:89)
    command (airflow/cli/cli_parser.py:48)
    main (airflow/__main__.py:40)
    <module> (airflow:8)
```

`py-spy dump` for the child `airflow scheduler` process

```
Python v3.8.7

Thread 16232 (idle): "MainThread"
    __enter__ (multiprocessing/synchronize.py:95)
    get (multiprocessing/queues.py:355)
    worker (multiprocessing/pool.py:114)
    run (multiprocessing/process.py:108)
    _bootstrap (multiprocessing/process.py:315)
    _launch (multiprocessing/popen_fork.py:75)
    __init__ (multiprocessing/popen_fork.py:19)
    _Popen (multiprocessing/context.py:277)
    start (multiprocessing/process.py:121)
    _repopulate_pool_static (multiprocessing/pool.py:326)
    _repopulate_pool (multiprocessing/pool.py:303)
    __init__ (multiprocessing/pool.py:212)
    Pool (multiprocessing/context.py:119)
    _send_tasks_to_celery (airflow/executors/celery_executor.py:330)
    _process_tasks (airflow/executors/celery_executor.py:272)
    trigger_tasks (airflow/executors/celery_executor.py:263)
    heartbeat (airflow/executors/base_executor.py:158)
    _run_scheduler_loop (airflow/jobs/scheduler_job.py:1388)
    _execute (airflow/jobs/scheduler_job.py:1284)
    run (airflow/jobs/base_job.py:237)
    scheduler (airflow/cli/commands/scheduler_command.py:63)
    wrapper (airflow/utils/cli.py:89)
    command (airflow/cli/cli_parser.py:48)
    main (airflow/__main__.py:40)
    <module> (airflow:8)
```

airflow/jobs/scheduler_job.py
return len(event_buffer)
        
        filter_for_tis = TI.filter_for_tis(tis_with_right_state)
        tis: List[TI] = session.query(TI).filter(filter_for_tis).options(selectinload('dag_model')).all()
        for ti in tis:
            try_number = ti_primary_key_to_try_number_map[ti.key.primary]
            buffer_key = ti.key.with_try_number(try_number)
            state, info = event_buffer.pop(buffer_key)
            
            if state == State.QUEUED:
                ti.external_executor_id = info
                self.log.info("Setting external_id for %s to %s", ti, info)
                continue
            if ti.try_number == buffer_key.try_number and ti.state == State.QUEUED:
                Stats.incr('scheduler.tasks.killed_externally')
                msg = (
                    "Executor reports task instance %s finished (%s) although the "
                    "task says its %s. (Info: %s) Was the task killed externally?"
                )
                self.log.error(msg, ti, state, ti.state, info)
                request = TaskCallbackRequest(
                    full_filepath=ti.dag_model.fileloc,
                    simple_task_instance=SimpleTaskInstance(ti),
                    msg=msg % (ti, state, ti.state, info),
                )
                self.log.info('Setting task instance %s state to %s as reported by executor', ti, state)
                ti.set_state(state)
                self.processor_agent.send_callback_to_execute(request)
        return len(event_buffer)
    def _execute(self) -> None: