**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
"""
        An wrapper for cli functions. It assumes to have Namespace instance
        at 1st positional argument
        :param args: Positional argument. It assumes to have Namespace instance
            at 1st positional argument
        :param kwargs: A passthrough keyword argument
        """
        _check_cli_args(args)
        metrics = _build_metrics(f.__name__, args[0])
        cli_action_loggers.on_pre_execution(**metrics)
        try:
            return f(*args, **kwargs)
        except Exception as e:
            metrics['error'] = e
            raise
        finally:
            metrics['end_datetime'] = datetime.utcnow()
            cli_action_loggers.on_post_execution(**metrics)
    return cast(T, wrapper)
def _build_metrics(func_name, namespace):