[SPARK-27492][DOC][FOLLOWUP] Update resource scheduling user docs

### What changes were proposed in this pull request?

Fix a config name typo from the resource scheduling user docs. In case users might get confused with the wrong config name, we'd better fix this typo.

### How was this patch tested?

Document change, no need to run test.

Closes apache#26047 from jiangxb1987/doc.

Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>

docs/configuration.md

@@ -2639,7 +2639,7 @@ Also, you can modify or add configurations at runtime:
 GPUs and other accelerators have been widely used for accelerating special workloads, e.g.,
 deep learning and signal processing. Spark now supports requesting and scheduling generic resources, such as GPUs, with a few caveats. The current implementation requires that the resource have addresses that can be allocated by the scheduler. It requires your cluster manager to support and be properly configured with the resources.
 
-There are configurations available to request resources for the driver: <code>spark.driver.resource.{resourceName}.amount</code>, request resources for the executor(s): <code>spark.executor.resource.{resourceName}.amount</code> and specify the requirements for each task: <code>spark.task.resource.{resourceName}.amount</code>. The <code>spark.driver.resource.{resourceName}.discoveryScript</code> config is required on YARN, Kubernetes and a client side Driver on Spark Standalone. <code>spark.driver.executor.{resourceName}.discoveryScript</code> config is required for YARN and Kubernetes. Kubernetes also requires <code>spark.driver.resource.{resourceName}.vendor</code> and/or <code>spark.executor.resource.{resourceName}.vendor</code>. See the config descriptions above for more information on each.
+There are configurations available to request resources for the driver: <code>spark.driver.resource.{resourceName}.amount</code>, request resources for the executor(s): <code>spark.executor.resource.{resourceName}.amount</code> and specify the requirements for each task: <code>spark.task.resource.{resourceName}.amount</code>. The <code>spark.driver.resource.{resourceName}.discoveryScript</code> config is required on YARN, Kubernetes and a client side Driver on Spark Standalone. <code>spark.executor.resource.{resourceName}.discoveryScript</code> config is required for YARN and Kubernetes. Kubernetes also requires <code>spark.driver.resource.{resourceName}.vendor</code> and/or <code>spark.executor.resource.{resourceName}.vendor</code>. See the config descriptions above for more information on each.
 
 Spark will use the configurations specified to first request containers with the corresponding resources from the cluster manager. Once it gets the container, Spark launches an Executor in that container which will discover what resources the container has and the addresses associated with each resource. The Executor will register with the Driver and report back the resources available to that Executor. The Spark scheduler can then schedule tasks to each Executor and assign specific resource addresses based on the resource requirements the user specified. The user can see the resources assigned to a task using the <code>TaskContext.get().resources</code> api. On the driver, the user can see the resources assigned with the SparkContext <code>resources</code> call. It's then up to the user to use the assignedaddresses to do the processing they want or pass those into the ML/AI framework they are using.