## Problem Description and Formulation

The problem requires maximizing the objective function \(4.43M + 3.06P\), where \(M\) represents the number of monochrome printers and \(P\) represents the number of postage stamps. The problem is subject to several constraints:

1. **Usefulness Ratings**: 
   - Monochrome printers have a usefulness rating of 12 each (\(r0_{x0} = 12\)).
   - Postage stamps have a usefulness rating of 13 each (\(r0_{x1} = 13\)).

2. **Constraints**:
   - The total combined usefulness rating must be greater than or equal to 38: \(12M + 13P \geq 38\).
   - The combination of monochrome printers and postage stamps must satisfy: \(-8M + 4P \geq 0\).
   - The total combined usefulness rating must be less than or equal to 72: \(12M + 13P \leq 72\).
   - Implicitly, the problem also states the total usefulness should be exactly 72 at maximum, but this seems to be a reiteration of the upper bound.

3. **Integer Requirements**:
   - \(M\) and \(P\) must be integers.

## Gurobi Code Formulation

Given the problem description, we can formulate the Gurobi code as follows:

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    M = model.addVar(name="monochrome_printers", vtype=gurobi.GRB.INTEGER)
    P = model.addVar(name="postage_stamps", vtype=gurobi.GRB.INTEGER)

    # Objective function: Maximize 4.43M + 3.06P
    model.setObjective(4.43 * M + 3.06 * P, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(12 * M + 13 * P >= 38, name="usefulness_rating_min")
    model.addConstr(-8 * M + 4 * P >= 0, name="monochrome_postage_balance")
    model.addConstr(12 * M + 13 * P <= 72, name="usefulness_rating_max")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Monochrome Printers: {M.varValue}")
        print(f"Postage Stamps: {P.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

This code defines the optimization problem with the specified objective function and constraints, solves it using Gurobi, and prints out the optimal solution if one is found.