torch.nn.functional.gelu returns NaN for +inf input with approximate="none"

### 🐛 Describe the bug

## Describe the issue

`torch.nn.functional.gelu` returns `NaN` for positive infinity input when `approximate="none"`.

For GELU with the exact formulation, `gelu(+inf)` should produce `+inf`, but PyTorch returns `NaN`.

The issue appears only on the `+inf` element. Other finite values match the expected output.

### Minimal reproducible example
```
import numpy as np
import torch
import torch.nn.functional as F

x = torch.tensor(
[
[float("nan"), float("inf"), -float("inf")],
[-0.026216749101877213, 0.11177391558885574, -0.04212268441915512],
],
dtype=torch.float32,
)

out = F.gelu(x, approximate="none")

print(out)
print("out[0, 1]:", out[0, 1])
print("is inf:", torch.isinf(out[0, 1]).item())
print("is nan:", torch.isnan(out[0, 1]).item())

```
### Actual result
```
tensor([[ nan, nan, nan],
[-0.0128, 0.0609, -0.0204]])

out[0, 1]: tensor(nan)
is inf: False
is nan: True
```

Expected out[0, 1] should be +inf, but actual output is NaN.

### Versions

## Environment

```
PyTorch version: 2.12.0.dev20260407+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.3 LTS (x86_64)
Python version: 3.11.15
Is CUDA available: True
GPU models and configuration:
GPU 0: NVIDIA RTX 6000 Ada Generation
GPU 1: NVIDIA RTX 6000 Ada Generation

Nvidia driver version: 570.211.01
numpy==2.4.4
torch==2.12.0.dev20260407+cu128
torchvision==0.27.0.dev20260407+cu128
torchaudio==2.11.0.dev20260407+cu128
triton==3.7.0+git9c288bc5
```

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki