[Kotomi-Du]

Details:

• [GPU] Refactor for FC Compressed  #38697 added handling for an OC-broadcast decompression scale/zero-point ([1, G] / [G, 1]) in the oneDNN FullyConnected implementation: get_grouped_mask() clears the OC bit from the grouped scale/zp mask whenever the OC dim of the scale/zp is 1.
• That check didn't consider whether the weights' N is also 1, in which case the scale isn't really broadcast. For example, u4 weights [1, 2048] with a transposed scale [1, 16] (group size 128) lost the OC bit; for a 2D FC that leaves mask = 1, K only. oneDNN's gemm doesn't support that mask, so oneDNN falls back to the reference ocl:ref:any matmul, a performance regression for this dynamic-shape FC.
• On re-evaluation, the OC-broadcast handling is overly defensive. The standard weight-compression flow (NNCF/optimum) always produces per-OC scale/zp [N, G, 1], which ConvertFullyConnectedToFullyConnectedCompressed reshapes to [N, G], so the scale/zp OC dim always matches the weights' N.
• This PR removes get_grouped_mask() and restores the full grouped mask for grouped scale/zp in both create() and load(), i.e. the behavior before [GPU] Refactor for FC Compressed  #38697. It also removes the compressed_int8_broadcast_scale_zp unit tests, which covered only the removed path.

Tickets:

• CVS-197314

AI Assistance:

• AI assistance used: yes
• Used AI to trace oneDNN matmul implementation selection (gemm_t vs ref_t), analyze the scale/zp mask computed by get_grouped_mask(), check where scale/zp shapes come from in the compression pipeline, and draft this description.

[sungeunk]

LGTM. Fixed perf issue on B70