**Apache Airflow version**: 2.1.0


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.18.17-gke.1901

**Environment**:

- **Cloud provider or hardware configuration**: Google Cloud
- **OS** (e.g. from /etc/os-release): Debian GNU/Linux 10 (buster)
- **Kernel** (e.g. `uname -a`): Linux airflow-scheduler-7697b66974-m6mct 5.4.89+ #1 SMP Sat Feb 13 19:45:14 PST 2021 x86_64 GNU/Linux
- **Install tools**:
- **Others**:

**What happened**:
When the `scheduler` is restarted the currently running tasks are facing SIGTERM error.
Every time the `scheduler` is restarted or re-deployed then the current `scheduler` is terminated and a new `scheduler` is created. If during this process exist tasks running the new `scheduler` will terminate these tasks with `complete` status and new tasks will be created to continue the work of the terminated ones. After few seconds the new tasks are terminated with `error` status and SIGTERM error.

<details><summary>Error log</summary> [2021-07-07 14:59:49,024] {cursor.py:661} INFO - query execution done
[2021-07-07 14:59:49,025] {arrow_result.pyx:0} INFO - fetching data done
[2021-07-07 15:00:07,361] {local_task_job.py:196} WARNING - State of this instance has been externally set to failed. Terminating instance.
[2021-07-07 15:00:07,363] {process_utils.py:100} INFO - Sending Signals.SIGTERM to GPID 150
[2021-07-07 15:00:12,845] {taskinstance.py:1264} ERROR - Received SIGTERM. Terminating subprocesses.
[2021-07-07 15:00:12,907] {process_utils.py:66} INFO - Process psutil.Process(pid=150, status='terminated', exitcode=0, started='14:59:46') (150) terminated with exit code 0 </details>

**What you expected to happen**:

The tasks currently running should be allowed to finish their process or the substitute tasks should execute their process with success.
The new `scheduler` should interfere with the running tasks.

**How to reproduce it**:

To reproduce is necessary to start a DAG that has some task(s) that take some minutes to be completed. During this task(s) processing a new deploy for `scheduler` should be executed. During the re-deploy, the current `scheduler` will be terminated and a new one will be created. The current task(s) will be completed (without finish their processing) and substituted for new ones that will fail in seconds.

**Anything else we need to know**:

The problem was not happening with Airflow 1.10.15 and it started to happens after the upgrade to Airflow 2.1.0.
airflow/jobs/scheduler_job.py
#
#
#
#
import datetime
import itertools
import logging
import multiprocessing
import os
import signal
import sys
import time
import warnings
from collections import defaultdict
from datetime import timedelta
from typing import Collection, DefaultDict, Dict, Iterator, List, Optional, Tuple
from sqlalchemy import and_, func, not_, or_, tuple_
from sqlalchemy.exc import OperationalError
from sqlalchemy.orm import load_only, selectinload
from sqlalchemy.orm.session import Session, make_transient
from airflow import models, settings
from airflow.configuration import conf
from airflow.dag_processing.manager import DagFileProcessorAgent
from airflow.executors.executor_loader import UNPICKLEABLE_EXECUTORS
from airflow.jobs.base_job import BaseJob
from airflow.models import DAG
from airflow.models.dag import DagModel