Hi Airflow Team,

I am running the Airflow in an EC2 instance which is installed by conda-forge during the Code Deploy.
After upgrading the Airflow version from 2.0.2 to >=2.1.0, I am facing an error every time when I try to start the scheduler in daemon mode using this command: ```airflow scheduler --daemon```
I took a look at a similar issue #11456 and try to fix it with Python 3.8.10 and `python-daemon` 2.3.0 but still doesn't work.
The webserver is working fine, but it can't detect the scheduler.



```
Traceback (most recent call last):
  File "/home/ec2-user/anaconda3/envs/airflow_env/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 2336, in _wrap_pool_connect
    return fn()
  File "/home/ec2-user/anaconda3/envs/airflow_env/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 364, in connect
    return _ConnectionFairy._checkout(self)
  File "/home/ec2-user/anaconda3/envs/airflow_env/lib/python3.8/site-packages/sqlalchemy/pool/base.py", line 809, in _checkout
    result = pool._dialect.do_ping(fairy.connection)
  File "/home/ec2-user/anaconda3/envs/airflow_env/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 575, in do_ping
    cursor.execute(self._dialect_specific_select_one)
psycopg2.OperationalError: SSL SYSCALL error: Socket operation on non-socket
```
Relevant package version:
`sqlalchemy`=1.3.23
`psycopg2`=2.8.6
`python-daemon`=2.3.0
`apache-airflow-providers-http`=2.0.0
`apache-airflow-providers-elasticsearch`=2.0.2

airflow/cli/commands/scheduler_command.py
#
#
"""Scheduler command"""
import signal
from multiprocessing import Process
from typing import Optional
import daemon
from daemon.pidfile import TimeoutPIDLockFile
from airflow import settings
from airflow.jobs.scheduler_job import SchedulerJob
from airflow.utils import cli as cli_utils
from airflow.utils.cli import process_subdir, setup_locations, setup_logging, sigint_handler, sigquit_handler
@cli_utils.action_logging
def scheduler(args):
    """Starts Airflow Scheduler"""
    skip_serve_logs = args.skip_serve_logs
    print(settings.HEADER)
    job = SchedulerJob(
        subdir=process_subdir(args.subdir),
        num_runs=args.num_runs,
        do_pickle=args.do_pickle,
    )
    if args.daemon:
        pid, stdout, stderr, log_file = setup_locations(
            "scheduler", args.pid, args.stdout, args.stderr, args.log_file
        )