## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'protein bars' and 'strawberries', which we can denote as $x_0$ and $x_1$ respectively. The objective function to maximize is $2x_0 + 4x_1$. The constraints are as follows:
- $14x_0 + 3x_1 \geq 6$ (sourness index)
- $2x_0 + 14x_1 \geq 23$ (milligrams of iron)
- $4x_0 + 12x_1 \geq 23$ (milligrams of calcium)
- $11x_0 + 3x_1 \geq 5$ (grams of protein)
- $4x_0 - 9x_1 \geq 0$
- $14x_0 + 3x_1 \leq 17$ (sourness index upper bound)
- $2x_0 + 14x_1 \leq 39$ (milligrams of iron upper bound)
- $4x_0 + 12x_1 \leq 44$ (milligrams of calcium upper bound)
- $11x_0 + 3x_1 \leq 26$ (grams of protein upper bound)
- $x_0$ is an integer
- $x_1$ is a continuous variable

## Step 2: Convert the problem into a Gurobi-compatible format
We need to define the variables, the objective function, and the constraints in a way that Gurobi can understand.

## 3: Write down the symbolic representation
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',
    '2*x0 + 14*x1 >= 23',
    '4*x0 + 12*x1 >= 23',
    '11*x0 + 3*x1 >= 5',
    '4*x0 - 9*x1 >= 0',
    '14*x0 + 3*x1 <= 17',
    '2*x0 + 14*x1 <= 39',
    '4*x0 + 12*x1 <= 44',
    '11*x0 + 3*x1 <= 26'
]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this problem using Gurobi in Python.

```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="protein_bars", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="strawberries")

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

    # Define the constraints
    model.addConstr(14 * x0 + 3 * x1 >= 6, name="sourness_index")
    model.addConstr(2 * x0 + 14 * x1 >= 23, name="iron")
    model.addConstr(4 * x0 + 12 * x1 >= 23, name="calcium")
    model.addConstr(11 * x0 + 3 * x1 >= 5, name="protein")
    model.addConstr(4 * x0 - 9 * x1 >= 0, name="protein_bar_strawberry_ratio")
    model.addConstr(14 * x0 + 3 * x1 <= 17, name="sourness_index_upper_bound")
    model.addConstr(2 * x0 + 14 * x1 <= 39, name="iron_upper_bound")
    model.addConstr(4 * x0 + 12 * x1 <= 44, name="calcium_upper_bound")
    model.addConstr(11 * x0 + 3 * x1 <= 26, name="protein_upper_bound")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Protein bars: {x0.varValue}")
        print(f"Strawberries: {x1.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```