[BUG] Completely broken on numpy 2.x (wrong vector sent to fitness())

With numpy 2.x, the fitness function is called with the wrong values in the decision vector x.

```
import pygmo as pg
class sphere_function:
def fitness(self, x):
cost = sum(x * x)
print(f"FITNESS EVAL: x = {x}, cost = {cost}")
return [cost]

def get_bounds(self):
return ([-1] * 3, [1] * 3)
prob = pg.problem(sphere_function())
pop = pg.population(prob, 5)
print(pop)
```

The example above results in the following. Notice how the population member #0 is [-0.504554, 0.87294, 0.639481] but the fitness function gets [-0.50455375 -0.50455375 -0.50455375].

```
FITNESS EVAL: x = [-0.50455375 -0.50455375 -0.50455375], cost = 0.7637234652148235
FITNESS EVAL: x = [0.4426457 0.4426457 0.4426457], cost = 0.5878056348538054
FITNESS EVAL: x = [-0.47532954 -0.47532954 -0.47532954], cost = 0.6778145209225833
FITNESS EVAL: x = [-0.29921424 -0.29921424 -0.29921424], cost = 0.2685874763420541
FITNESS EVAL: x = [0.63765334 0.63765334 0.63765334], cost = 1.219805338218631
Problem name:
C++ class name: pybind11::object

Global dimension: 3
Integer dimension: 0
Fitness dimension: 1
Number of objectives: 1
Equality constraints dimension: 0
Inequality constraints dimension: 0
Lower bounds: [-1, -1, -1]
Upper bounds: [1, 1, 1]
Has batch fitness evaluation: false

Has gradient: false
User implemented gradient sparsity: false
Has hessians: false
User implemented hessians sparsity: false

Fitness evaluations: 5

Thread safety: none

Population size: 5

List of individuals:
#0:
ID: 14351618211829196000
Decision vector: [-0.504554, 0.87294, 0.639481]
Fitness vector: [0.763723]
#1:
ID: 4233680892357765143
Decision vector: [0.442646, 0.462569, -0.970743]
Fitness vector: [0.587806]
#2:
ID: 457977541108690035
Decision vector: [-0.47533, 0.97677, -0.805264]
Fitness vector: [0.677815]
#3:
ID: 3780462174932513958
Decision vector: [-0.299214, -0.134968, -0.699614]
Fitness vector: [0.268587]
#4:
ID: 12888162120884326344
Decision vector: [0.637653, 0.840588, -0.0665339]
Fitness vector: [1.21981]

Champion decision vector: [-0.299214, -0.134968, -0.699614]
Champion fitness: [0.268587]
````
On numpy 1.x, we get :

```
FITNESS EVAL: x = [-0.38570461 -0.2763677 -0.19182114], cost = 0.2619425019598244
FITNESS EVAL: x = [-0.20674582 -0.56690414 -0.68899929], cost = 0.8388441636353365
FITNESS EVAL: x = [-0.57724158 0.56498671 0.34808134], cost = 0.77357844517818
FITNESS EVAL: x = [-0.13960828 0.79237103 0.04465593], cost = 0.6493364798439734
FITNESS EVAL: x = [-0.19441917 0.14853349 0.61230586], cost = 0.4347794724943631

Population size: 5

List of individuals:
#0:
ID: 4942601127234171947
Decision vector: [-0.385705, -0.276368, -0.191821]
Fitness vector: [0.261943]
#1:
ID: 11313699314120481030
Decision vector: [-0.206746, -0.566904, -0.688999]
Fitness vector: [0.838844]
#2:
ID: 8331669631110857304
Decision vector: [-0.577242, 0.564987, 0.348081]
Fitness vector: [0.773578]
#3:
ID: 3796521727361963477
Decision vector: [-0.139608, 0.792371, 0.0446559]
Fitness vector: [0.649336]
#4:
ID: 18100786683321109976
Decision vector: [-0.194419, 0.148533, 0.612306]
Fitness vector: [0.434779]
```

- OS: ubuntu
- Installation method: uv
- Version: v2.19.5

Start with the provided sphere_function reproduction under NumPy 2.x and trace the population evaluation path that calls fitness(). Verify that each call receives the full decision vector shown for that population member, then rerun the example to confirm the fitness values match those vectors.

Written by the indexing model from the issue text.