## Problem Description and Formulation

The problem is a linear programming optimization problem. The goal is to minimize the cost of a mixture of shrimp and beef that meets certain nutritional requirements.

Let's define the variables:

* $x$: the amount of shrimp in kilograms
* $y$: the amount of beef in kilograms

The objective function is to minimize the total cost:

* Cost of shrimp: $36x$
* Cost of beef: $40y$
* Total cost: $36x + 40y$

The constraints are:

* Protein requirement: $2.5x + 4y \geq 20$
* Fat requirement: $3x + 2.5y \geq 25$
* Non-negativity constraints: $x \geq 0, y \geq 0$

## Gurobi Code

```python
import gurobi

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

    # Define the variables
    x = model.addVar(name="shrimp", lb=0, ub=None)
    y = model.addVar(name="beef", lb=0, ub=None)

    # Define the objective function
    model.setObjective(36 * x + 40 * y, sense=gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(2.5 * x + 4 * y >= 20, name="protein_requirement")
    model.addConstr(3 * x + 2.5 * y >= 25, name="fat_requirement")

    # Optimize the model
    model.optimize()

    # Check if the model is feasible
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Shrimp: {x.varValue} kg")
        print(f"Beef: {y.varValue} kg")
        print(f"Minimum cost: ${model.objVal:.2f}")
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("No feasible solution found.")
    else:
        print("Optimization failed.")

solve_sausage_problem()
```