To convert the given problem into a symbolic representation and then solve it using Gurobi, we first need to identify the variables, objective function, and constraints.

### Symbolic Representation

- **Variables:**
  - $x_1$: The amount of ravioli.
  - $x_2$: The number of cornichons.

- **Objective Function:**
  - Minimize $1.89x_1 + 6.47x_2$

- **Constraints:**
  1. Healthiness rating constraint: $7x_1 + 3x_2 \geq 8$
  2. Calcium constraint: $x_1 + 7x_2 \geq 13$
  3. Carbohydrates constraint: $7x_1 + 4x_2 \geq 23$
  4. Linear constraint: $x_1 - 2x_2 \geq 0$
  5. Maximum healthiness rating constraint: $7x_1 + 3x_2 \leq 24$
  6. Maximum calcium constraint: $x_1 + 7x_2 \leq 32$
  7. Maximum carbohydrates constraint: $7x_1 + 4x_2 \leq 46$

### Symbolic Representation in JSON Format

```json
{
  'sym_variables': [('x1', 'ravioli'), ('x2', 'cornichons')],
  'objective_function': 'Minimize 1.89*x1 + 6.47*x2',
  'constraints': [
    '7*x1 + 3*x2 >= 8',
    'x1 + 7*x2 >= 13',
    '7*x1 + 4*x2 >= 23',
    'x1 - 2*x2 >= 0',
    '7*x1 + 3*x2 <= 24',
    'x1 + 7*x2 <= 32',
    '7*x1 + 4*x2 <= 46'
  ]
}
```

### Gurobi Code

```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="cornichons")

# Set objective function
m.setObjective(1.89*x1 + 6.47*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(7*x1 + 3*x2 >= 8, "healthiness_rating")
m.addConstr(x1 + 7*x2 >= 13, "calcium_constraint")
m.addConstr(7*x1 + 4*x2 >= 23, "carbohydrates_constraint")
m.addConstr(x1 - 2*x2 >= 0, "linear_constraint")
m.addConstr(7*x1 + 3*x2 <= 24, "max_healthiness_rating")
m.addConstr(x1 + 7*x2 <= 32, "max_calcium")
m.addConstr(7*x1 + 4*x2 <= 46, "max_carbohydrates")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Ravioli: {x1.x}")
    print(f"Cornichons: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```