To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of packets of sardines to buy
- $x_2$ as the number of packets of tuna to buy

The objective function is to minimize the total cost, which can be represented as:
\[ 15x_1 + 18x_2 \]

The constraints based on the nutritional requirements are:

1. Total fat: $4x_1 + 6x_2 \geq 800$
2. Total essential fatty acids: $12x_1 + 10x_2 \geq 1200$
3. Total protein: $10x_1 + 7x_2 \geq 700$

Additionally, since we cannot buy a negative number of packets, we have:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

Given this setup, the symbolic representation in JSON format is:

```json
{
    'sym_variables': [('x1', 'number of packets of sardines'), ('x2', 'number of 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']
}
```

Now, let's implement this problem in Gurobi using Python:

```python
from gurobipy import *

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

# Create variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="sardines", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="tuna", lb=0)

# Set the objective function
m.setObjective(15*x1 + 18*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(4*x1 + 6*x2 >= 800, "fat_requirement")
m.addConstr(12*x1 + 10*x2 >= 1200, "essential_fatty_acids_requirement")
m.addConstr(10*x1 + 7*x2 >= 700, "protein_requirement")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of packets of sardines: {x1.x}")
    print(f"Number of packets of tuna: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found")
```