Here's the formulation and the Gurobi code to solve the sausage mixture problem:

**Decision Variables:**

* `x`: Kilograms of pork used in the mixture.
* `y`: Kilograms of chicken used in the mixture.

**Objective Function:**

Minimize the total cost:  `40x + 50y`

**Constraints:**

* **Protein Constraint:** `2x + 3y >= 10` (At least 10 units of protein)
* **Fat Constraint:** `4x + 2y >= 15` (At least 15 units of fat)
* **Non-negativity Constraints:** `x >= 0`, `y >= 0` (Cannot use negative amounts of pork or chicken)


```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
m = gp.Model("sausage_mixture")

# Create variables
x = m.addVar(lb=0, name="pork") # Kilograms of pork
y = m.addVar(lb=0, name="chicken") # Kilograms of chicken

# Set objective function
m.setObjective(40*x + 50*y, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x + 3*y >= 10, "protein_req")
m.addConstr(4*x + 2*y >= 15, "fat_req")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal cost: ${m.objVal:.2f}")
    print(f"Kilograms of pork: {x.x:.2f}")
    print(f"Kilograms of chicken: {y.x:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
