To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of packets of cat food as $x_1$ and the number of cans of tuna as $x_2$. The objective is to minimize the total cost, which can be represented by the function $12x_1 + 7x_2$.

The constraints are based on the nutritional requirements:
- Carbohydrates: $5x_1 + 7x_2 \geq 700$
- Vitamins: $15x_1 + 12x_2 \geq 1100$
- Protein: $12x_1 + 15x_2 \geq 900$

Since we cannot buy negative quantities of cat food or tuna, we also have the non-negativity constraints:
- $x_1 \geq 0$
- $x_2 \geq 0$

Thus, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'packets of cat food'), ('x2', 'cans of tuna')],
    'objective_function': '12*x1 + 7*x2',
    'constraints': [
        '5*x1 + 7*x2 >= 700',
        '15*x1 + 12*x2 >= 1100',
        '12*x1 + 15*x2 >= 900',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

To solve this linear programming problem using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="packets_of_cat_food")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="cans_of_tuna")

# Set the objective function
m.setObjective(12*x1 + 7*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(5*x1 + 7*x2 >= 700, "carbohydrates_constraint")
m.addConstr(15*x1 + 12*x2 >= 1100, "vitamins_constraint")
m.addConstr(12*x1 + 15*x2 >= 900, "protein_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Packets of cat food: {x1.x}")
    print(f"Cans of tuna: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found")
```