### Apache Airflow version

Other Airflow 2 version (please specify below)

### What happened

Airflow version: **2.5.3**

Related to this comment from @vchiapaikeo: https://github.com/apache/airflow/pull/33172#issuecomment-1677501450

A couple of mins after midnight UTC - when 100s of DAGs are kicked off - we noticed our triggerer replicas failing liveness probe checks and restarting systematically.

Further profiling led to the discovery that the triggerer’s sync loop hangs for several minutes when there are 1000s of triggers running simultaneously, specifically while [bulk fetching triggers](https://github.com/apache/airflow/blob/v2-5-test/airflow/jobs/triggerer_job.py#L398), which causes the triggerer to miss heartbeats and eventually get restarted by k8s.

With profiling still enabled, we observed that while the trigger is hanging and we profile the execution, we get this stack trace:

```
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           [506/45463]
        1    0.000    0.000   29.928   29.928 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/query.py:2757(all)               
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1468(all)      
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:395(_allrows)  
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1388(_fetchall_impl)       
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1808(_fetchall_impl)                                                                                                                                                                                                                                                                                                                                                                                
        2    0.000    0.000   29.922   14.961 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/loading.py:135(chunks)
        1    0.000    0.000   29.921   29.921 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:390(_raw_all_rows)
        1    0.001    0.001   29.921   29.921 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:393(<listcomp>)                                                                                                                                                                                                                                                                                                                                                                                                                        
      125    0.000    0.000   29.919    0.239 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/sql/type_api.py:1711(process) 
      125    0.002    0.000   29.915    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/utils/sqlalchemy.py:146(process_result_value)  
      125    0.001    0.000   29.909    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/utils/sqlalchemy.py:122(db_supports_json)    
      125    0.001    0.000   29.908    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:562(get)                  
      125    0.000    0.000   29.907    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:732(_get_environment_variables)
      125    0.002    0.000   29.907    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:478(_get_env_var_option)
      125    0.002    0.000   29.902    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:103(run_command)                
      125    0.001    0.000   29.786    0.238 /usr/local/lib/python3.10/subprocess.py:1110(communicate)                                                       
      125    0.006    0.000   29.785    0.238 /usr/local/lib/python3.10/subprocess.py:1952(_communicate)                                             
      250    0.003    0.000   29.762    0.119 /usr/local/lib/python3.10/selectors.py:403(select)                                         
      250   29.758    0.119   29.758    0.119 {method 'poll' of 'select.poll' objects}                                                                  
      125    0.002    0.000    0.100    0.001 /usr/local/lib/python3.10/subprocess.py:758(__init__)                                                                  
      125    0.004    0.000    0.094    0.001 /usr/local/lib/python3.10/subprocess.py:1687(_execute_child)
```

Which indicates that airflow is running a subprocess for each fetched row and that takes the vast majority of the execution time.

We found that during the unmarshaling of the resulting rows into the Trigger model, the [kwargs column](https://github.com/apache/airflow/blob/v2-5-test/airflow/models/trigger.py#L57) (ExtendedJSON) runs [process_returned_value](https://github.com/apache/airflow/blob/v2-5-test/airflow/utils/sqlalchemy.py#L146), on each row, and reads the `SQL_ALCHEMY_CONN` configuration to determine whether the engine supports json or not and parse kwargs accordingly. However, in our case we define `SQL_ALCHEMY_CONN_CMD` as opposed to `SQL_ALCHEMY_CONN`, which causes the sync loop to spawn a new subprocess for every row ([here](https://github.com/apache/airflow/blob/v2-5-test/airflow/configuration.py#L485-L488)).

We workaround it by using `SQL_ALCHEMY_CONN` instead of `SQL_ALCHEMY_CONN_CMD`, as it involves reading an environment variable instead of spawning a new subprocess.

### What you think should happen instead

The triggerer model caches caches either the `SQL_ALCHEMY_CONN` or the [db_supports_json](https://github.com/apache/airflow/blob/v2-5-stable/airflow/utils/sqlalchemy.py#L122) property.

### How to reproduce

Simultaneously kick off 100s of DAGs with at least a few deferrable operators each and use `SQL_ALCHEMY_CONN_CMD` instead of `SQL_ALCHEMY_CONN`

### Operating System

Debian GNU/Linux 11 (bullseye)

### Versions of Apache Airflow Providers

apache-airflow-providers-airbyte==3.2.0
apache-airflow-providers-alibaba==2.2.0
apache-airflow-providers-amazon==7.3.0
apache-airflow-providers-apache-beam==4.3.0
apache-airflow-providers-apache-cassandra==3.1.1
apache-airflow-providers-apache-drill==2.3.1
apache-airflow-providers-apache-druid==3.3.1
apache-airflow-providers-apache-hdfs==3.2.0
apache-airflow-providers-apache-hive==5.1.3
apache-airflow-providers-apache-kylin==3.1.0
apache-airflow-providers-apache-livy==3.3.0
apache-airflow-providers-apache-pig==4.0.0
apache-airflow-providers-apache-pinot==4.0.1
apache-airflow-providers-apache-spark==4.0.0
apache-airflow-providers-apache-sqoop==3.1.1
apache-airflow-providers-arangodb==2.1.1
apache-airflow-providers-asana==2.1.0
apache-airflow-providers-atlassian-jira==2.0.1
apache-airflow-providers-celery==3.1.0
apache-airflow-providers-cloudant==3.1.0
apache-airflow-providers-cncf-kubernetes==5.2.2
apache-airflow-providers-common-sql==1.3.4
apache-airflow-providers-databricks==4.0.0
apache-airflow-providers-datadog==3.1.0
apache-airflow-providers-dbt-cloud==3.1.0
apache-airflow-providers-dingding==3.1.0
apache-airflow-providers-discord==3.1.0
apache-airflow-providers-docker==3.5.1
apache-airflow-providers-elasticsearch==4.4.0
apache-airflow-providers-exasol==4.1.3
apache-airflow-providers-facebook==3.1.0
apache-airflow-providers-ftp==3.3.1
apache-airflow-providers-github==2.2.1
apache-airflow-providers-google==8.11.0
apache-airflow-providers-grpc==3.1.0
apache-airflow-providers-hashicorp==3.3.0
apache-airflow-providers-http==4.2.0
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-influxdb==2.1.0
apache-airflow-providers-jdbc==3.3.0
apache-airflow-providers-jenkins==3.2.0
apache-airflow-providers-microsoft-azure==5.2.1
apache-airflow-providers-microsoft-mssql==3.3.2
apache-airflow-providers-microsoft-psrp==2.2.0
apache-airflow-providers-microsoft-winrm==3.1.1
apache-airflow-providers-mongo==3.1.1
apache-airflow-providers-mysql==4.0.2
apache-airflow-providers-neo4j==3.2.1
apache-airflow-providers-odbc==3.2.1
apache-airflow-providers-openfaas==3.1.0
apache-airflow-providers-opsgenie==5.0.0
apache-airflow-providers-oracle==3.6.0
apache-airflow-providers-pagerduty==3.1.0
apache-airflow-providers-papermill==3.1.1
apache-airflow-providers-plexus==3.1.0
apache-airflow-providers-postgres==5.4.0
apache-airflow-providers-presto==4.2.2
apache-airflow-providers-qubole==3.3.1
apache-airflow-providers-redis==3.1.0
apache-airflow-providers-salesforce==5.3.0
apache-airflow-providers-samba==4.1.0
apache-airflow-providers-segment==3.1.0
apache-airflow-providers-sendgrid==3.1.0
apache-airflow-providers-sftp==4.2.4
apache-airflow-providers-singularity==3.1.0
apache-airflow-providers-slack==7.2.0
apache-airflow-providers-snowflake==4.0.4
apache-airflow-providers-sqlite==3.3.1
apache-airflow-providers-ssh==3.5.0
apache-airflow-providers-tableau==4.1.0
apache-airflow-providers-tabular==1.1.0
apache-airflow-providers-telegram==4.0.0
apache-airflow-providers-trino==4.3.2
apache-airflow-providers-vertica==3.3.1
apache-airflow-providers-yandex==3.3.0
apache-airflow-providers-zendesk==4.2.0


### Deployment

Other 3rd-party Helm chart

### Deployment details

Chart based on the official helm chart. Airflow running on Google Kubernetes Engine (GKE) using `KubernetesExecutor`.

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""
        Process DateTimes from the DB making sure to always return UTC.
        Not using timezone.convert_to_utc as that converts to configured TIMEZONE
        while the DB might be running with some other setting. We assume UTC
        datetimes in the database.
        """
        if value is not None:
            if value.tzinfo is None:
                value = value.replace(tzinfo=utc)
            else:
                value = value.astimezone(utc)
        return value
    def load_dialect_impl(self, dialect):
        if dialect.name == "mssql":
            return mssql.DATETIME2(precision=6)
        elif dialect.name == "mysql":
            return mysql.TIMESTAMP(fsp=6)
        return super().load_dialect_impl(dialect)
class ExtendedJSON(TypeDecorator):
    """
    A version of the JSON column that uses the Airflow extended JSON serialization.
    See airflow.serialization.
    """
    impl = Text
    cache_ok = True
    def db_supports_json(self):