**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
"""
    Builds metrics dict from function args
    It assumes that function arguments is from airflow.bin.cli module's function
    and has Namespace instance where it optionally contains "dag_id", "task_id",
    and "execution_date".
    :param func_name: name of function
    :param namespace: Namespace instance from argparse
    :return: dict with metrics
    """
    from airflow.models import Log
    sub_commands_to_check = {'users', 'connections'}
    sensitive_fields = {'-p', '--password', '--conn-password'}
    full_command = list(sys.argv)
    sub_command = full_command[1] if len(full_command) > 1 else None
    if sub_command in sub_commands_to_check:
        for idx, command in enumerate(full_command):
            if command in sensitive_fields:
                
                full_command[idx + 1] = "*" * 8
            else:
                
                for sensitive_field in sensitive_fields: