To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the given information.

### Symbolic Representation

Let's denote:
- $x_1$ as the number of ravioli,
- $x_2$ as the quantity of strawberries.

The objective function is to maximize $2x_1 + 4x_2$.

Constraints are as follows:
1. Total combined umami index: $7x_1 + 6x_2 \geq 26$
2. Total combined sourness index: $5x_1 + 6x_2 \geq 11$
3. Total calcium from ravioli and strawberries: $2x_1 + 5x_2 \geq 21$
4. Linear constraint: $x_1 - 8x_2 \geq 0$
5. Maximum total combined umami index: $7x_1 + 6x_2 \leq 84$
6. Maximum total combined sourness index: $5x_1 + 6x_2 \leq 59$
7. Maximum calcium from ravioli and strawberries: $2x_1 + 5x_2 \leq 65$

Symbolic representation 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'
    ]
}
```

### Gurobi Code

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

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="ravioli")
x2 = m.addVar(vtype=GRB.INTEGER, name="strawberries")  # Ensure strawberries are integers

# Set objective function
m.setObjective(2*x1 + 4*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 6*x2 >= 26, "umami_index_min")
m.addConstr(5*x1 + 6*x2 >= 11, "sourness_index_min")
m.addConstr(2*x1 + 5*x2 >= 21, "calcium_min")
m.addConstr(x1 - 8*x2 >= 0, "linear_constraint")
m.addConstr(7*x1 + 6*x2 <= 84, "umami_index_max")
m.addConstr(5*x1 + 6*x2 <= 59, "sourness_index_max")
m.addConstr(2*x1 + 5*x2 <= 65, "calcium_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Ravioli: {x1.x}, Strawberries: {x2.x}")
else:
    print("No optimal solution found.")
```