To solve the given optimization problem, we need to maximize the objective function while satisfying all the constraints. The objective function is to maximize `1.66 * milkshakes + 8.63 * peanutbutter_sandwiches`. 

The constraints are:
- Each milkshake contains 8.76 grams of protein.
- Each peanut butter sandwich contains 11.41 grams of protein.
- The total combined grams of protein from both must be at least 36.
- The expression `1 * milkshakes - 9 * peanutbutter_sandwiches >= 0` must hold true.
- The total combined grams of protein from both must not exceed 58.

Given that there can be fractional numbers of milkshakes and peanut butter sandwiches, we treat these variables as continuous. 

We'll use Gurobi's Python interface to model this problem. Here's how you could set it up:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define the variables
milkshakes = m.addVar(lb=0, name="milkshakes", vtype=GRB.CONTINUOUS)
peanutbutter_sandwiches = m.addVar(lb=0, name="peanutbutter_sandwiches", vtype=GRB.CONTINUOUS)

# Define the objective function
m.setObjective(1.66 * milkshakes + 8.63 * peanutbutter_sandwiches, GRB.MAXIMIZE)

# Add constraints
m.addConstr(8.76 * milkshakes + 11.41 * peanutbutter_sandwiches >= 36, name="protein_min")
m.addConstr(milkshakes - 9 * peanutbutter_sandwiches >= 0, name="milkshake_peanut_ratio")
m.addConstr(8.76 * milkshakes + 11.41 * peanutbutter_sandwiches <= 58, name="protein_max")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milkshakes: {milkshakes.x}")
    print(f"Peanut butter sandwiches: {peanutbutter_sandwiches.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```