## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'lemons' and 'chicken thighs', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $1.87 \times \text{lemons} + 1.19 \times \text{chicken thighs}$, which in symbolic notation is $1.87x_1 + 1.19x_2$.

## 3: Define the constraints in symbolic notation
The constraints given are:
- $\text{sourness index of lemons} + \text{sourness index of chicken thighs} \geq 9$: $5x_1 + 9x_2 \geq 9$
- $\text{healthiness rating of lemons} + \text{healthiness rating of chicken thighs} \geq 16$: $4x_1 + 13x_2 \geq 16$
- $-9 \times \text{lemons} + 2 \times \text{chicken thighs} \geq 0$: $-9x_1 + 2x_2 \geq 0$
- $\text{total combined sourness index} \leq 20$: $5x_1 + 9x_2 \leq 20$
- $\text{total combined healthiness rating} \leq 67$: $4x_1 + 13x_2 \leq 67$

## 4: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'lemons'), ('x2', 'chicken thighs')],
    'objective_function': '1.87*x1 + 1.19*x2',
    'constraints': [
        '5*x1 + 9*x2 >= 9',
        '4*x1 + 13*x2 >= 16',
        '-9*x1 + 2*x2 >= 0',
        '5*x1 + 9*x2 <= 20',
        '4*x1 + 13*x2 <= 67'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="lemons", lb=0)  # Assuming non-negative for practical purposes
    x2 = model.addVar(name="chicken thighs", lb=0)  # Assuming non-negative for practical purposes

    # Define the objective function
    model.setObjective(1.87 * x1 + 1.19 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(5 * x1 + 9 * x2 >= 9, name="sourness_index_constraint")
    model.addConstr(4 * x1 + 13 * x2 >= 16, name="healthiness_rating_constraint")
    model.addConstr(-9 * x1 + 2 * x2 >= 0, name="healthiness_sourness_tradeoff_constraint")
    model.addConstr(5 * x1 + 9 * x2 <= 20, name="max_sourness_index_constraint")
    model.addConstr(4 * x1 + 13 * x2 <= 67, name="max_healthiness_rating_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Lemons: {x1.varValue}")
        print(f"Chicken thighs: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```