To solve the given optimization problem, we first need to define the symbolic representation of the variables and the objective function, followed by the constraints. 

Given:
- Variables: `x0` for protein bars and `x1` for strawberries.
- Objective Function: Maximize `2*x0 + 4*x1`.
- Constraints:
  - Total sourness index: `14*x0 + 3*x1 >= 6` and `14*x0 + 3*x1 <= 17`.
  - Total milligrams of iron: `2*x0 + 14*x1 >= 23` and `2*x0 + 14*x1 <= 39`.
  - Total milligrams of calcium: `4*x0 + 12*x1 >= 23` and `4*x0 + 12*x1 <= 44`.
  - Total grams of protein: `11*x0 + 3*x1 >= 5` and `11*x0 + 3*x1 <= 26`.
  - Additional constraint: `4*x0 - 9*x1 >= 0`.

The symbolic representation of the problem is:
```json
{
  'sym_variables': [('x0', 'protein bars'), ('x1', 'strawberries')],
  'objective_function': '2*x0 + 4*x1',
  'constraints': [
    '14*x0 + 3*x1 >= 6',
    '14*x0 + 3*x1 <= 17',
    '2*x0 + 14*x1 >= 23',
    '2*x0 + 14*x1 <= 39',
    '4*x0 + 12*x1 >= 23',
    '4*x0 + 12*x1 <= 44',
    '11*x0 + 3*x1 >= 5',
    '11*x0 + 3*x1 <= 26',
    '4*x0 - 9*x1 >= 0'
  ]
}
```

Now, let's implement the solution using Gurobi in Python:

```python
from gurobipy import *

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

# Add variables to the model
x0 = model.addVar(vtype=GRB.INTEGER, name="protein_bars")
x1 = model.addVar(vtype=GRB.CONTINUOUS, name="strawberries")

# Set the objective function
model.setObjective(2*x0 + 4*x1, GRB.MAXIMIZE)

# Add constraints to the model
model.addConstr(14*x0 + 3*x1 >= 6, "sourness_index_min")
model.addConstr(14*x0 + 3*x1 <= 17, "sourness_index_max")
model.addConstr(2*x0 + 14*x1 >= 23, "iron_min")
model.addConstr(2*x0 + 14*x1 <= 39, "iron_max")
model.addConstr(4*x0 + 12*x1 >= 23, "calcium_min")
model.addConstr(4*x0 + 12*x1 <= 44, "calcium_max")
model.addConstr(11*x0 + 3*x1 >= 5, "protein_min")
model.addConstr(11*x0 + 3*x1 <= 26, "protein_max")
model.addConstr(4*x0 - 9*x1 >= 0, "additional_constraint")

# Optimize the model
model.optimize()

# Print the solution
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Protein Bars: {x0.x}")
    print(f"Strawberries: {x1.x}")
    print(f"Objective Function Value: {model.objVal}")
else:
    print("No optimal solution found")
```