**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/log/file_task_handler.py
if self.handler:
            self.handler.emit(record)
    def flush(self):
        if self.handler:
            self.handler.flush()
    def close(self):
        if self.handler:
            self.handler.close()
    def _render_filename(self, ti, try_number):
        if self.filename_jinja_template:
            if hasattr(ti, 'task'):
                jinja_context = ti.get_template_context()
                jinja_context['try_number'] = try_number
            else:
                jinja_context = {
                    'ti': ti,
                    'ts': ti.execution_date.isoformat(),
                    'try_number': try_number,
                }
            return self.filename_jinja_template.render(**jinja_context)
        return self.filename_template.format(
            dag_id=ti.dag_id,
            task_id=ti.task_id,
            execution_date=ti.execution_date.isoformat(),
            try_number=try_number,
        )
    def _read_grouped_logs(self):