Speeding up problem set-up

This is more a suggestion than an actual bug.

I've been using python-mip and cvxpy on the same MI problems, and even though their actual problem-solving performances are usually the same (or even slightly better for python-mip), the problem set-up (defining the objective and the constraints) is much too slow with python-mip.

For instance, let's consider the following two implementations:

```python
A : np.ndarray of shape (2000, 3069)
d : np.ndarray of shape (3069,)

# python-mip
# the following block takes 11 seconds to run
model = mip.Model("disorders")
x = model.add_var_tensor(shape=(len(d),), name="x", var_type=mip.BINARY)
model.objective = mip.minimize(x.T @ d)
model += (A @ x == 1)

# this line takes 0.13s to run
status = model.optimize()

# cvxpy
# this blocks takes 0.14s
x = cp.Variable(shape=len(d), boolean=True)
obj = cvxpy.Minimize(d.T @ x)
constraints = [A @ x == 1]
prob = cvxpy.Problem(obj, constraints)
optimal = prob.solve()
```

It's quite a shame that python-mip is limited by such a non-central part of MIP-solving, that is, setting up the problem.
I used CProfile to figure out what's taking so much time when building the problem, and it seems that the repeated instance checks when multiplying a variable with a scalar are the culprits.

Maybe you could exploit the structured nature of `LinExprTensor` to have only one typecheck when multiplying then with arrays... or something like this.