Fix memory consumption issue with quantized Gemini Nano2 models on CPU

[evkotov]

Details:

Problem:
Quantized models (i8/fp16 weights) were consuming excessive memory (up to 90GB) due to
ConstantFolding transformation converting compressed weights to fp32.

Root cause:

1. EinsumDecomposition was not called in CPU pipeline before MarkDequantization
2. MarkDequantization couldn't recognize decompression patterns with Einsum operations
3. DisableDecompressionConvertConstantFolding was disabled, allowing unwanted conversions

Solution:

1. Add EinsumDecomposition to decompression_handling_manager before MarkDequantization
   This allows proper pattern recognition for Einsum operations
2. Keep DisableDecompressionConvertConstantFolding enabled (comment out the disable line)
   This preserves the protection against unwanted constant folding

Transformation pipeline flow:
Before fix:
MarkDequantization -> [Einsum blocks pattern] -> ConstantFolding converts to fp32

After fix:
EinsumDecomposition -> MarkDequantization -> [Pattern recognized] -> Constants preserved

Test results on einsum_model_with_fp16_i8:

• Before: constants converted to fp32 (4x memory increase for i8)
• After: constants remain in i8 format (1057 MB memory usage)

Both changes are required - applying only one results in incorrect behavior.

Tickets:

• 165827