To solve the given optimization problem, we first need to establish a symbolic representation of the variables and the objective function. The variables in this problem are 'fruit salads' and 'bagged salads', which we can represent symbolically as \(x_1\) and \(x_2\), respectively.

Given:
- Objective Function: Minimize \(4x_1 + 4x_2\)
- Constraints:
  1. Cost constraints for each type of salad: \(28x_1\) for fruit salads, \(11x_2\) for bagged salads.
  2. Sourness index constraints for each type of salad: \(9x_1\) for fruit salads, \(29x_2\) for bagged salads.
  3. Total cost must be at least $30: \(28x_1 + 11x_2 \geq 30\).
  4. Spend at least $30 on each type of salad individually is not a standard constraint but rather seems to be part of ensuring the total cost meets or exceeds $30.
  5. Total sourness index must be at least 47: \(9x_1 + 29x_2 \geq 47\).
  6. Additional linear constraint: \(8x_1 - 2x_2 \geq 0\).
  7. Maximum total cost: \(28x_1 + 11x_2 \leq 89\) (since the problem mentions an upper bound of $89 for 'r0').
  8. Maximum total sourness index: \(9x_1 + 29x_2 \leq 241\) (since the problem mentions an upper bound of 241 for 'r1', but then it also specifies a constraint related to 123, which seems inconsistent; we will proceed with the information provided in the context).
  9. Quantity of fruit salads does not have to be an integer: \(x_1 \in \mathbb{R}\).
  10. Quantity of bagged salads must be an integer: \(x_2 \in \mathbb{Z}\).

However, upon closer inspection, there seems to be a mix-up in interpreting some constraints directly from the provided context and problem description. The maximum cost constraint mentioned in the step-by-step breakdown is $47, but the original context mentions an upper bound of $89 for 'r0' (dollar cost). For sourness, it mentions needing to be "as much or more than 47" and also references a maximum of 123, which seems to be a mix-up. Given this, we'll clarify and proceed with what's directly stated in the optimization problem description.

The symbolic representation given these corrections would be:

```json
{
    'sym_variables': [('x1', 'fruit salads'), ('x2', 'bagged salads')],
    'objective_function': '4*x1 + 4*x2',
    'constraints': [
        '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'
    ]
}
```

And now, here's the Gurobi code that captures this problem:

```python
from gurobipy import *

# Create a model
m = Model("Salad_Optimization")

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="fruit_salads")
x2 = m.addVar(vtype=GRB.INTEGER, name="bagged_salads")

# Set objective function
m.setObjective(4*x1 + 4*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(28*x1 + 11*x2 >= 30, name="total_cost_min")
m.addConstr(9*x1 + 29*x2 >= 47, name="sourness_index_min")
m.addConstr(8*x1 - 2*x2 >= 0, name="additional_linear_constraint")
m.addConstr(28*x1 + 11*x2 <= 89, name="total_cost_max")
m.addConstr(9*x1 + 29*x2 <= 123, name="sourness_index_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"fruit_salads: {x1.x}")
    print(f"bagged_salads: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```