## Step 1: Define the symbolic representation of the problem

Let's denote the amount of shrimp in kilograms as \(x_1\) and the amount of beef in kilograms as \(x_2\). The objective is to minimize the cost, which is \(36x_1 + 40x_2\). The constraints are based on the nutritional requirements: protein and fat.

## Step 2: Formulate the objective function and constraints

- **Objective Function:** Minimize \(36x_1 + 40x_2\)
- **Constraints:**
  1. Protein constraint: \(2.5x_1 + 4x_2 \geq 20\)
  2. Fat constraint: \(3x_1 + 2.5x_2 \geq 25\)
  3. Non-negativity constraints: \(x_1 \geq 0, x_2 \geq 0\)

## 3: Symbolic Representation

The symbolic representation can be described as:
```json
{
'sym_variables': [('x1', 'shrimp'), ('x2', 'beef')],
'objective_function': '36*x1 + 40*x2',
'constraints': [
    '2.5*x1 + 4*x2 >= 20',
    '3*x1 + 2.5*x2 >= 25',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 4: Convert to Gurobi Code

Now, let's convert this into Gurobi code in Python:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="shrimp", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="beef", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(36 * x1 + 40 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2.5 * x1 + 4 * x2 >= 20, name="protein_constraint")
    model.addConstr(3 * x1 + 2.5 * x2 >= 25, name="fat_constraint")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal cost: ", model.objVal)
        print("Shrimp (kg): ", x1.x)
        print("Beef (kg): ", x2.x)
    else:
        print("The problem is infeasible")

solve_sausage_problem()
```