## Step 1: Define the symbolic representation of the variables
The variables are 'fruit salads' and 'bagged salads', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $4x_1 + 4x_2$.

## Step 3: Convert the constraints into symbolic representations
The constraints given are:
1. $28x_1 + 11x_2 \geq 30$
2. $28x_1 + 11x_2 \geq 30$ (This seems to be a repeat of the first constraint, but it's actually stated as "You must spend at least $30 on fruit salads, and bagged salads.")
3. $9x_1 + 29x_2 \geq 47$
4. $8x_1 - 2x_2 \geq 0$
5. $28x_1 + 11x_2 \leq 89$
6. $9x_1 + 29x_2 \leq 123$

## 4: Consider the variable bounds and types
- $x_1$ (fruit salads) can be any real number (not necessarily an integer).
- $x_2$ (bagged salads) must be an integer.

## 5: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'fruit salads'), ('x2', 'bagged salads')],
'objective_function': '4*x1 + 4*x2',
'constraints': [
    '28*x1 + 11*x2 >= 30',
    '28*x1 + 11*x2 >= 30',
    '9*x1 + 29*x2 >= 47',
    '8*x1 - 2*x2 >= 0',
    '28*x1 + 11*x2 <= 89',
    '9*x1 + 29*x2 <= 123'
]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="fruit_salads", lb=0)  # Assuming no lower bound for x1
    x2 = model.addVar(name="bagged_salads", lb=0, vtype=gurobi.GRB.INTEGER)

    # Objective function: minimize 4*x1 + 4*x2
    model.setObjective(4*x1 + 4*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(28*x1 + 11*x2 >= 30, name="total_cost_min")
    model.addConstr(9*x1 + 29*x2 >= 47, name="total_sourness_min")
    model.addConstr(8*x1 - 2*x2 >= 0, name="sourness_balance")
    model.addConstr(28*x1 + 11*x2 <= 89, name="total_cost_max")
    model.addConstr(9*x1 + 29*x2 <= 123, name="total_sourness_max")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Fruit Salads: {x1.varValue}")
        print(f"Bagged Salads: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```