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

Let's denote:
- $x_1$ as the number of packets of sardines
- $x_2$ as the number of packets of tuna

The objective is to minimize the cost: $15x_1 + 18x_2$

The constraints based on the nutritional requirements are:
- Fat: $4x_1 + 6x_2 \geq 800$
- Essential fatty acids: $12x_1 + 10x_2 \geq 1200$
- Protein: $10x_1 + 7x_2 \geq 700$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

Since the problem asks for the minimum cost, we consider the constraints as greater than or equal to the required amounts.

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

The symbolic representation is:
```json
{
'sym_variables': [('x1', 'packets of sardines'), ('x2', 'packets of tuna')],
'objective_function': '15*x1 + 18*x2',
'constraints': [
    '4*x1 + 6*x2 >= 800',
    '12*x1 + 10*x2 >= 1200',
    '10*x1 + 7*x2 >= 700',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 3: Write the Gurobi code in Python

```python
import gurobi

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

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

    # Define the objective function
    model.setObjective(15*x1 + 18*x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(4*x1 + 6*x2 >= 800, name="fat_constraint")
    model.addConstr(12*x1 + 10*x2 >= 1200, name="essential_fatty_acids_constraint")
    model.addConstr(10*x1 + 7*x2 >= 700, name="protein_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Packets of sardines: {x1.varValue}")
        print(f"Packets of tuna: {x2.varValue}")
        print(f"Minimum cost: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_aquarium_problem()
```