[Mlas] optimize MlasConv using thread partition opt

[zoeczy]

Description

This PR enhances MlasConv in ONNX Runtime by introducing a thread partitioning strategy based on Batch Size (bs) and Group Count (group). This change improves multi-threading efficiency in convolution scenarios where scaling with core/thread count was previously limited.

The PR also includes updates to the bench_sconv utility to support and evaluate multi-threaded performance under the new partitioning strategy.

• Command to run multi-threaded benchmarks under core binding: numactl -C core_num0-core_num_1 ./onnxruntime_mlas_benchmark --benchmark_filter=Teams
• The following results demonstrate the performance improvement of the optimized MlasConv under a 4-thread configuration:
  
  Compared to the current master implementation, the optimized version exhibits nearly 3× performance improvement, showing effective scaling with thread count. In contrast, the master branch shows no meaningful performance gain when increasing the number of threads, due to insufficient parallelization in the original implementation.

Motivation and Context

• Previously, MlasConv exhibited minimal performance gains when increasing the number of threads or CPU cores in scenarios with small batch sizes or grouped convolutions.
• This change introduces a finer-grained workload distribution across threads by splitting the work along batch and group dimensions.
• Benchmarks using bench_sconv show a noticeable performance improvement in multi-threaded runs, especially on multi-core CPUs.

Releated Issues

#25152

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