To solve the optimization problem described, we first need to establish a clear symbolic representation of the variables and the constraints involved. 

The variables are:
- `x1`: The amount of lemons.
- `x2`: The number of chicken thighs.

Given this, we can express the objective function as: 
\[ \text{Minimize} \quad 1.87x_1 + 1.19x_2 \]

And the constraints are:
1. Total sourness index is at least 9: \(5x_1 + 9x_2 \geq 9\)
2. Total sourness index is no more than 20: \(5x_1 + 9x_2 \leq 20\)
3. Total healthiness rating is at least 16: \(4x_1 + 13x_2 \geq 16\)
4. Total healthiness rating is no more than 67: \(4x_1 + 13x_2 \leq 67\)
5. The linear constraint: \(-9x_1 + 2x_2 \geq 0\)

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

Now, let's implement the solution in Gurobi Python:
```python
from gurobipy import *

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

# Add variables to the model
x1 = model.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="lemons")
x2 = model.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.INTEGER, name="chicken_thighs")

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

# Add constraints to the model
model.addConstr(5*x1 + 9*x2 >= 9, "Total_Sourness_Index_At_Least_9")
model.addConstr(5*x1 + 9*x2 <= 20, "Total_Sourness_Index_No_More_Than_20")
model.addConstr(4*x1 + 13*x2 >= 16, "Total_Healthiness_Rating_At_Least_16")
model.addConstr(4*x1 + 13*x2 <= 67, "Total_Healthiness_Rating_No_More_Than_67")
model.addConstr(-9*x1 + 2*x2 >= 0, "Linear_Constraint")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print(f"Optimal solution: {model.objVal}")
    print(f"Lemons: {x1.x}, Chicken Thighs: {x2.x}")
else:
    print("No optimal solution found")
```