Capacity-Scheduler Not Running multiple Jobs submitted in different queues - amazon-web-services

Question

So we are new to Capacity scheduler. We are spinning up an AWS Cluster where we want to add capacity scheduler configuration to have the jobs running simultaneously in different different queue.
The issue is, even though we are managing to create a stable cluster with the scheduler config, but then we are unable to submit the jobs parallely in each queue.
By referring to the below link , we created the configurations by providing he respective values.
https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html
With all parameters added as given in the above link, the cluster fails while bootstrapping.
yarn.scheduler.capacity.maximum-am-resource-percent=0.2
yarn.scheduler.capacity.maximum-applications=10000
yarn.scheduler.capacity.node-locality-delay=40
yarn.scheduler.capacity.root.accessible-node-labels=*
yarn.scheduler.capacity.root.acl_administer_queue=yarn
yarn.scheduler.capacity.root.capacity=100
yarn.scheduler.capacity.root.default.acl_submit_applications=yarn
yarn.scheduler.capacity.root.default.capacity=50
yarn.scheduler.capacity.root.default.maximum-capacity=100
yarn.scheduler.capacity.root.default.state=RUNNING
yarn.scheduler.capacity.root.default.user-limit-factor=2
yarn.scheduler.capacity.root.queues=bt,default,opt
yarn.scheduler.capacity.queue-mappings-override.enable=false
yarn.scheduler.capacity.root.bt.acl_administer_queue=*
yarn.scheduler.capacity.root.bt.acl_submit_applications=*
yarn.scheduler.capacity.root.bt.capacity=25
yarn.scheduler.capacity.root.bt.maximum-capacity=100
yarn.scheduler.capacity.root.bt.minimum-user-limit-percent=100
yarn.scheduler.capacity.root.bt.ordering-policy=fair
yarn.scheduler.capacity.root.bt.ordering-policy.fair.enable-size-based-weight=false
yarn.scheduler.capacity.root.bt.priority=0
yarn.scheduler.capacity.root.bt.state=RUNNING
yarn.scheduler.capacity.root.bt.user-limit-factor=1
yarn.scheduler.capacity.root.default.acl_administer_queue=yarn
yarn.scheduler.capacity.root.default.minimum-user-limit-percent=25
yarn.scheduler.capacity.root.default.ordering-policy=fair
yarn.scheduler.capacity.root.default.ordering-policy.fair.enable-size-based-weight=false
yarn.scheduler.capacity.root.default.priority=0
yarn.scheduler.capacity.root.opt.acl_administer_queue=*
yarn.scheduler.capacity.root.opt.acl_submit_applications=*
yarn.scheduler.capacity.root.opt.capacity=25
yarn.scheduler.capacity.root.opt.maximum-capacity=25
yarn.scheduler.capacity.root.opt.minimum-user-limit-percent=100
yarn.scheduler.capacity.root.opt.ordering-policy=fair
yarn.scheduler.capacity.root.opt.ordering-policy.fair.enable-size-based-weight=false
yarn.scheduler.capacity.root.opt.priority=0
yarn.scheduler.capacity.root.opt.state=RUNNING
yarn.scheduler.capacity.root.opt.user-limit-factor=1
yarn.scheduler.capacity.root.priority=0
Some how with above configuration we are able to create a cluster but some issues which we are facing are:
1. The job runs perfectly fine in default queue and if submitted in other queues, its stuck in ACCEPTED state.
2. Only one job is getting submitted at a time and other jobs are still waiting in EMR steps instead of running under different different queues.
P.S: The jobs we are submitting on to the EMRs are spark jobs triggered from Lambda function.