## Step 1: Define the symbolic representation of the problem
The problem involves mixing pork and chicken to create sausages with specific nutritional requirements at a minimum cost. Let's define the symbolic variables:
- $x_1$ = amount of pork in kg
- $x_2$ = amount of chicken in kg

The objective is to minimize the cost, which can be represented as $40x_1 + 50x_2$.

## Step 2: Define the constraints based on the nutritional requirements
The mixture needs to contain at least 10 units of protein and 15 units of fat.
- Protein constraint: $2x_1 + 3x_2 \geq 10$
- Fat constraint: $4x_1 + 2x_2 \geq 15$

Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the amounts of pork and chicken cannot be negative.

## 3: Symbolic representation of the optimization problem
```json
{
'sym_variables': [('x1', 'pork in kg'), ('x2', 'chicken in kg')],
'objective_function': '40*x1 + 50*x2',
'constraints': [
    '2*x1 + 3*x2 >= 10',
    '4*x1 + 2*x2 >= 15',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 4: Translate the symbolic representation into Gurobi code in Python
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="pork", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="chicken", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(40 * x1 + 50 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2 * x1 + 3 * x2 >= 10, name="protein_constraint")
    model.addConstr(4 * x1 + 2 * x2 >= 15, name="fat_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal cost: ", model.objVal)
        print("Pork: ", x1.varValue, "kg")
        print("Chicken: ", x2.varValue, "kg")
    else:
        print("The model is infeasible")

solve_sausage_mixture_problem()
```