## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'ravioli' and 'strawberries', which we can represent symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $2 \times \text{ravioli} + 4 \times \text{strawberries}$, which in symbolic notation is $2x_1 + 4x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $7x_1 + 6x_2 \geq 26$ (minimum combined umami index)
2. $5x_1 + 6x_2 \geq 11$ (minimum combined sourness index)
3. $2x_1 + 5x_2 \geq 21$ (minimum combined milligrams of calcium)
4. $x_1 - 8x_2 \geq 0$ (relationship between ravioli and strawberries)
5. $7x_1 + 6x_2 \leq 84$ (maximum combined umami index)
6. $5x_1 + 6x_2 \leq 59$ (maximum combined sourness index)
7. $2x_1 + 5x_2 \leq 65$ (maximum combined milligrams of calcium)
8. $x_2 \in \mathbb{Z}$ (strawberries must be an integer)

## 4: Define the symbolic representation of the problem in JSON format
```json
{
    'sym_variables': [('x1', 'ravioli'), ('x2', 'strawberries')],
    'objective_function': '2*x1 + 4*x2',
    'constraints': [
        '7*x1 + 6*x2 >= 26',
        '5*x1 + 6*x2 >= 11',
        '2*x1 + 5*x2 >= 21',
        'x1 - 8*x2 >= 0',
        '7*x1 + 6*x2 <= 84',
        '5*x1 + 6*x2 <= 59',
        '2*x1 + 5*x2 <= 65'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="ravioli", lb=0)  # ravioli can be non-negative
x2 = model.addVar(name="strawberries", lb=0, integrality=gurobi.GRB.INTEGER)  # strawberries must be an integer

# Define the objective function
model.setObjective(2 * x1 + 4 * x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(7 * x1 + 6 * x2 >= 26)  # minimum combined umami index
model.addConstr(5 * x1 + 6 * x2 >= 11)  # minimum combined sourness index
model.addConstr(2 * x1 + 5 * x2 >= 21)  # minimum combined milligrams of calcium
model.addConstr(x1 - 8 * x2 >= 0)  # relationship between ravioli and strawberries
model.addConstr(7 * x1 + 6 * x2 <= 84)  # maximum combined umami index
model.addConstr(5 * x1 + 6 * x2 <= 59)  # maximum combined sourness index
model.addConstr(2 * x1 + 5 * x2 <= 65)  # maximum combined milligrams of calcium

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Ravioli: {x1.varValue}")
    print(f"Strawberries: {x2.varValue}")
    print(f"Objective: {model.objVal}")
else:
    print("No optimal solution found.")
```