tf.data.experimental.service.DispatchServer segfaults after normal start/shutdown sequence

import tensorflow as tf
import numpy as np

def generate_data(num_samples=1000, num_features=10):
    x = np.random.rand(num_samples, num_features).astype(np.float32)
    y = np.random.randint(0, 2, size=(num_samples,)).astype(np.int32)
    return x, y

def create_dataset(x, y):
    dataset = tf.data.Dataset.from_tensor_slices((x, y))
    dataset = dataset.shuffle(buffer_size=1000).batch(32)
    return dataset

def start_dispatch_server(start=True):
    return tf.data.experimental.service.DispatchServer(start=start)

x, y = generate_data()
dataset = create_dataset(x, y)

dispatch_server = start_dispatch_server(start=True)
dispatch_server = start_dispatch_server(start=False)

print(f"Dispatch server started at: {dispatch_server.target}")

### Standalone code to reproduce the issue

```shell
Environment requirements

Ubuntu 24.04.3 LTS x86_64
Python 3.10
TensorFlow 2.21.0
NumPy 2.2.6
Reproduced in local conda environment: tf_test

conda create -n tf_test python=3.10 -y
conda activate tf_test
pip install tensorflow==2.21.0 numpy==2.2.6

conda run --no-capture-output -n tf_test python repro.py

(tf_test) user@user-Standard-PC-Q35-ICH9-2009:/home/lhj$ python /home/lhj/callChainBuild/src_tensorflow/gen_program/validation_results_rerun/crashes/4300.py
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1774354528.998577 3824177 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
I0000 00:00:1774354529.146547 3824177 cpu_feature_guard.cc:227] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1774354530.615380 3824177 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
I0000 00:00:1774354532.205013 3824177 gpu_device.cc:2043] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9606 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
I0000 00:00:1774354532.206587 3824177 gpu_device.cc:2043] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 9606 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:02:00.0, compute capability: 7.5
I0000 00:00:1774354532.208159 3824177 gpu_device.cc:2043] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 9606 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:03:00.0, compute capability: 7.5
I0000 00:00:1774354532.209530 3824177 gpu_device.cc:2043] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 9606 MB memory:  -> device: 3, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:04:00.0, compute capability: 7.5
I0000 00:00:1774354532.437703 3824177 dispatcher_impl.cc:230] Started tf.data service dispatcher in non-fault-tolerant mode with config: goo.gle/debugonly   
protocol: "grpc"
job_gc_check_interval_ms: 600000
job_gc_timeout_ms: 300000
client_timeout_ms: 300000
worker_timeout_ms: 600000
worker_max_concurrent_snapshots: 3

It will not recover its state on restart.
I0000 00:00:1774354532.437809 3824177 server_lib.cc:88] Started tf.data DispatchServer running at 0.0.0.0:44709
I0000 00:00:1774354532.438009 3824177 server_lib.cc:100] Shut down DispatchServer server running at port 44709
Dispatch server started at: grpc://localhost:0
Segmentation fault (core dumped)