To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints algebraically.

Let's define:
- $x_1$ as the amount of Type A dog food in kg.
- $x_2$ as the amount of Type B dog food in kg.

The objective is to minimize the total cost, which can be represented by the objective function: $2x_1 + 5x_2$.

The constraints are:
1. The mixture must contain a minimum of 12 units of meat: $1x_1 + 3x_2 \geq 12$.
2. The mixture must contain a minimum of 8 units of micronutrients: $2x_1 + 1x_2 \geq 8$.
3. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$, because the amount of dog food cannot be negative.

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'Type A dog food in kg'), ('x2', 'Type B dog food in kg')],
    'objective_function': '2*x1 + 5*x2',
    'constraints': ['1*x1 + 3*x2 >= 12', '2*x1 + 1*x2 >= 8', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we'll use the following code:
```python
from gurobipy import *

# Create a new model
m = Model("Dog_Food_Optimization")

# Define variables
x1 = m.addVar(name="Type_A", vtype=GRB.CONTINUOUS, lb=0)
x2 = m.addVar(name="Type_B", vtype=GRB.CONTINUOUS, lb=0)

# Set the objective function
m.setObjective(2*x1 + 5*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(x1 + 3*x2 >= 12, name="Meat_Requirement")
m.addConstr(2*x1 + x2 >= 8, name="Micronutrients_Requirement")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Type A dog food: {x1.x} kg")
    print(f"Type B dog food: {x2.x} kg")
    print(f"Total cost: ${m.objVal}")
else:
    print("No optimal solution found.")
```