Error on mixing shapes of multiple enumerated shape inputs

## 🐞Describing the bug
I have a program with 3 inputs, all have a flexible enumerated shape, two share the same shape. The input shapes are:
- `shapes_1 = [(1, 3, 64, 16), (1, 3, 64, 32)] # last dimension shape varies, called Length1`
- `shapes_2 = [(1, 3, 64, 256), (1, 3, 64, 512)] # last dimension shape varies, called Length2`

After building, the model runs correctly with Length1=16 and Length2=256, or Length1=32 and Length2=512, but when I try to run with `Length1=32` and `Length2=256` it fails.

I tried extending the enumerated shapes, to match different combinations (setting `shape_1=[(1, 3, 64, 16), (1, 3, 64, 32), (1, 3, 64, 32), (1, 3, 64, 16)])` and analogous for `shape_2`), but it does not work.

## Stack Trace
Running on Python trace
```python
File ~/.pyenv/versions/apple/lib/python3.11/site-packages/coremltools/models/model.py:654, in MLModel.predict(self, data, state)
651 MLModel._check_predict_data(data)
653 if self.__proxy__:
--> 654 return self._get_predictions(self.__proxy__,
655 verify_and_convert_input_dict,
656 data,
657 state)
658 else: # Error case
659 if _macos_version() < (10, 13):

File ~/.pyenv/versions/apple/lib/python3.11/site-packages/coremltools/models/model.py:702, in MLModel._get_predictions(proxy, preprocess_method, data, state)
700 preprocess_method(data)
701 state = None if state is None else state.__proxy__
--> 702 return proxy.predict(data, state)
703 else:
704 assert type(data) == list

RuntimeError: Caught an unknown exception!
```

## To Reproduce
- Please add a minimal code example that can reproduce the error when running it.
```python
import numpy as np
import coremltools as ct
from coremltools.converters.mil import Builder as mb
import coremltools.converters.mil as mil

A = [16, 32]
B = [256, 512]
# A, B = np.repeat(A, len(B)), B * len(A)

q_seqlens = A
kv_seqlens = B
input_ids_shapes = [(1, 3, 64, seqlen) for seqlen in q_seqlens]
kv_shapes = [(1, 3, 64, seqlen) for seqlen in kv_seqlens]

input_ids_shape_def = mil.input_types.EnumeratedShapes(shapes=input_ids_shapes)
kv_shape_def = mil.input_types.EnumeratedShapes(shapes=kv_shapes)

@mb.program(
input_specs=[
mb.TensorSpec(input_ids_shape_def.symbolic_shape, mil.input_types.types.fp16),
mb.TensorSpec(kv_shape_def.symbolic_shape, mil.input_types.types.fp16),
mb.TensorSpec(kv_shape_def.symbolic_shape, mil.input_types.types.fp16),
],
opset_version=mil.builder.AvailableTarget.iOS18
)
def prog(
query, key_cache, value_cache,
):
scores = mb.matmul(x=query, y=key_cache, transpose_x=True)
scores = mb.mul(x=scores, y=np.float16(64 ** -0.5))
weights = mb.softmax(x=scores)
attention = mb.matmul(x=value_cache, y=weights, transpose_y=True)
return attention # , key_cache

cml_flex = ct.convert(
prog,
compute_units=ct.ComputeUnit.CPU_AND_NE,
compute_precision=ct.precision.FLOAT16,
minimum_deployment_target=ct.target.iOS18,
inputs=[
ct.TensorType(name="query", shape=ct.EnumeratedShapes(input_ids_shapes)),
ct.TensorType(name="key_cache", shape=ct.EnumeratedShapes(kv_shapes)),
ct.TensorType(name="value_cache", shape=ct.EnumeratedShapes(kv_shapes)),
],
)

QL = 16
CL = 512

np.random.seed(42)
cml_flex.predict({
'query': np.random.randn(1, 3, 64, QL).astype(np.float16),
'key_cache': np.random.randn(1, 3, 64, CL).astype(np.float16),
'value_cache': np.random.randn(1, 3, 64, CL).astype(np.float16),
})

```

## System environment (please complete the following information):
- coremltools version: 8.0b1
- OS (e.g. MacOS version or Linux type): 15.0 beta 2

## Additional context

Also, when I remove the last comment in the program (`#, key_cache`), when running a prediction it does not raise an exception when running with different shapes, but all inputs all converted to `0`s (`query` is an array of only `0` and same of `key_cache` and `value_cache`). I'll report this issue in Apple Forums and Feedback Assistant. But I wasn't sure if the first part of the problem is just a conversion issue or intrinsic CoreML issue, thus why I reported here also.

Additional Swift trace
```swift
*** Terminating app due to uncaught exception 'NSInternalInconsistencyException', reason: 'There is no function in the program library for the provided input=query = MultiArray : Float16 1 × 3 × 64 × 16 array
key_cache = MultiArray : Float16 1 × 3 × 64 × 512 array
value_cache = MultiArray : Float16 1 × 3 × 64 × 512 array
.'
*** First throw call stack:
(
0 CoreFoundation 0x00000001998f6920 __exceptionPreprocess + 176
1 libobjc.A.dylib 0x00000001993deb1c objc_exception_throw + 76
2 CoreFoundation 0x00000001998f6810 +[NSException exceptionWithName:reason:userInfo:] + 0
3 CoreML 0x00000001a3745a98 -[MLE5EnumeratedShapeExecutionStreamOperationPool takeOutOperationForFeatures:error:] + 480
4 CoreML 0x00000001a3845a78 -[MLE5ExecutionStream setupOperationForInputFeatures:operationPool:error:] + 92
5 CoreML 0x00000001a37f58d0 -[MLE5Engine _cleanUpAndReconfigureStream:forInputFeatures:error:] + 108
6 CoreML 0x00000001a37f4be8 -[MLE5Engine _predictionFromFeatures:options:completionHandler:] + 256
7 CoreML 0x00000001a37f511c -[MLE5Engine submitPredictionRequest:completionHandler:] + 124
8 CoreML 0x00000001a37cc780 __62-[MLDelegateModel _submitPredictionRequest:completionHandler:]_block_invoke + 420
9 libdispatch.dylib 0x00000001001b0b6c _dispatch_call_block_and_release + 32
10 libdispatch.dylib 0x00000001001b28ac _dispatch_client_callout + 20
11 libdispatch.dylib 0x00000001001b6110 _dispatch_continuation_pop + 700
12 libdispatch.dylib 0x00000001001b50ac _dispatch_async_redirect_invoke + 616
13 libdispatch.dylib 0x00000001001ca9b8 _dispatch_root_queue_drain + 404
14 libdispatch.dylib 0x00000001001cb5c4 _dispatch_worker_thread2 + 188
15 libsystem_pthread.dylib 0x000000010024d0c4 _pthread_wqthread + 228
16 libsystem_pthread.dylib 0x0000000100254cf0 start_wqthread + 8
)
libc++abi: terminating due to uncaught exception of type NSException
```

Begin with the minimal Python reproduction and inspect coremltools/models/model.py at MLModel.predict and _get_predictions. Compare the enumerated-shape combinations produced by ct.convert for query, key_cache, and value_cache; done means mixed valid lengths such as 16 and 512 execute without the CoreML exception and inputs are not converted to zeros.

Written by the indexing model from the issue text.