Improve CI speed and resolve issues of run_quantization_check

[james77777778]

I have analyzed the time cost in run_quantization_check

Test command: CUDA_VISIBLE_DEVICES= KERAS_BACKEND=jax pytest keras_nlp/src/ -k backbone_basics

	time cost
run_quantization_check=False	45.39s
run_quantization_check=True without calling quantize	47.45s
run_quantization_check=True with calling quantize	269s
run_quantization_check=True with calling quantize + saving	275s

Obviously, the bottleneck is the underlying computation when calling Model.quantize.

Here, I propose an improvement by pre-configuring DTypePolicyMap and using it to instantiate the model to avoid quantization-related computation.
This should improve the speed of CI.

Some minor bugs have been spotted and resolved too.

• Backbone.get_config() should consider that self.dtype_policy is already a DTypePolicyMap
• The name of self.embeddings_layer_norm in BloomBackbone has been changed to avoid a duplicated name that breaks DTypePolicyMap.
• OPTBackbone.get_config missed super()
• XLNetBackbone failed to pass run_quantization_check. (Will try to fix it in the future)

EDITED:
The CI is much faster now. (JAX: ~27mins -> 18mins)

[mattdangerw]

This looks great! Thank you!