Documentation of decision vector variable types ordering

Reading the [documentation](https://esa.github.io/pygmo2/problem.html#pygmo.problem), it does state with mathematical notation ($\mathbf{lb}, \mathbf{ub} \in \mathbb R^{n_{cx}} \times \mathbb Z^{n_{ix}}$) that the decision vector is composed by real variables followed by the integer part, however I think it would be good to explicitly state this with words in the docs.

To verify the order matters (not that I did it by mistake at first and this is how I realised):

```python
class Problem:
def get_bounds(self, ) -> tuple[np.ndarray, np.ndarray]:

# Some unoptimized code
box_bounds_lower = [0, 0, ..., 0, 0.0, ..., 0.0]
box_bounds_upper = [1, 1, ..., 1, 1.0, ..., 1.0]

return box_bounds_lower, box_bounds_upper

# Integer dec. variables before real raises value error
prob = pg.problem(Problem())

# Output:
ValueError:
function: check_problem_bounds
where: /root/install/pagmo2-2.19.0/src/problem.cpp, 104
what: An upper bound of the integer part of the decision vector is: 35.182177 and is not an integer.
```

By changing the bounds order, the error goes away:

```python
...
def get_bounds(self, ) -> tuple[np.ndarray, np.ndarray]:
return box_bounds_lower[::-1], box_bounds_upper[::-1]

prob = pg.problem(Problem())

# Output:
Problem name: SolarMED MINLP problem
C++ class name: pybind11::object

Global dimension: 78
Integer dimension: 24
Fitness dimension: 11
Number of objectives: 1
Equality constraints dimension: 0
Inequality constraints dimension: 10
Tolerances on constraints: [0, 0, 0, 0, 0, ... ]
Lower bounds: [0, 0, 0, 0, 0, ... ]
Upper bounds: [35.3206, 35.286, 35.1822, 0, 0, ... ]
Has batch fitness evaluation: false
```

Also, I wonder if since the data types of the box-bounds already need to match the data type of the decision vector, if it would not be possible to support an arbitrary ordering of the decision vector.

Best regards,
Juanmi