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

Let's denote the amount of Type A food and Type B food to be bought as $x_1$ and $x_2$ respectively. The objective is to minimize the cost, which is $2x_1 + 5x_2$. The constraints are:

- The mixture must contain a minimum of 12 units of meat: $x_1 + 3x_2 \geq 12$
- The mixture must contain a minimum of 8 units of micronutrients: $2x_1 + x_2 \geq 8$
- The amounts of food cannot be negative: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a Gurobi-compatible format

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'Type A food'), ('x2', 'Type B food')],
    'objective_function': '2*x1 + 5*x2',
    'constraints': [
        'x1 + 3*x2 >= 12',
        '2*x1 + x2 >= 8',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Write the Gurobi code to solve the problem

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="Type_A_food", lb=0, ub=float('inf'))
    x2 = model.addVar(name="Type_B_food", lb=0, ub=float('inf'))

    # Define the objective function
    model.setObjective(2*x1 + 5*x2, gurobi.MINIMIZE)

    # Define the constraints
    model.addConstr(x1 + 3*x2 >= 12, name="meat_constraint")
    model.addConstr(2*x1 + x2 >= 8, name="micronutrients_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("The problem is infeasible")

solve_dog_food_problem()
```