**Apache Airflow version**: 2.0.1

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): v1.17.4

**Environment**: Dev
- **OS** (e.g. from /etc/os-release): RHEL7

**What happened**:

After running fine for some time my airflow tasks got stuck in scheduled state with below error in Task Instance Details:
"All dependencies are met but the task instance is not running. In most cases this just means that the task will probably be scheduled soon unless: - The scheduler is down or under heavy load If this task instance does not start soon please contact your Airflow administrator for assistance."


**What you expected to happen**:

I restarted the scheduler then it started working fine. When i checked my metrics i realized the scheduler has a memory leak and over past 4 days it has reached up to 6GB of memory utilization

In version >2.0 we don't even have the run_duration config option to restart scheduler periodically to avoid this issue until it is resolved.

**How to reproduce it**:
I saw this issue in multiple dev instances of mine all running Airflow 2.0.1 on kubernetes with KubernetesExecutor. 
Below are the configs that i changed from the default config.
max_active_dag_runs_per_dag=32
parallelism=64
dag_concurrency=32
sql_Alchemy_pool_size=50
sql_Alchemy_max_overflow=30

**Anything else we need to know**:

The scheduler memory leaks occurs consistently in all instances i have been running. The memory utilization keeps growing for scheduler.

airflow/utils/cli.py
#
#
#
#
"""Utilities module for cli"""
import functools
import json
import logging
import os
import re
import socket
import sys
import threading
import traceback
import warnings
from argparse import Namespace
from datetime import datetime
from typing import TYPE_CHECKING, Callable, Optional, TypeVar, cast
from airflow import settings
from airflow.exceptions import AirflowException
from airflow.utils import cli_action_loggers
from airflow.utils.platform import getuser, is_terminal_support_colors
from airflow.utils.session import provide_session
T = TypeVar("T", bound=Callable)
if TYPE_CHECKING:
    from airflow.models import DAG
def _check_cli_args(args):