To tackle this optimization problem, we first need to define the symbolic representation as requested. Let's denote 'bagged salads' as \(x_0\) and 'knishes' as \(x_1\).

Given:
- The objective is to minimize \(5.3x_0 + 8.08x_1\).
- Constraints:
  - Iron content: \(12x_0 + 17x_1 \geq 29\) and \(12x_0 + 17x_1 \leq 78\).
  - Tastiness rating: \(x_0 + 22x_1 \geq 14\) and \(x_0 + 22x_1 \leq 40\).
  - Additional constraint: \(8x_0 - 5x_1 \geq 0\).

Symbolic Representation:
```json
{
  'sym_variables': [('x0', 'bagged salads'), ('x1', 'knishes')],
  'objective_function': 'minimize 5.3*x0 + 8.08*x1',
  'constraints': [
    '12*x0 + 17*x1 >= 29',
    '12*x0 + 17*x1 <= 78',
    '1*x0 + 22*x1 >= 14',
    '1*x0 + 22*x1 <= 40',
    '8*x0 - 5*x1 >= 0'
  ]
}
```

Now, let's implement this problem in Gurobi:

```python
from gurobipy import *

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

# Define the variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bagged_salads")
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="knishes")

# Set the objective function
m.setObjective(5.3*x0 + 8.08*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(12*x0 + 17*x1 >= 29, "iron_min")
m.addConstr(12*x0 + 17*x1 <= 78, "iron_max")
m.addConstr(x0 + 22*x1 >= 14, "taste_min")
m.addConstr(x0 + 22*x1 <= 40, "taste_max")
m.addConstr(8*x0 - 5*x1 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Bagged Salads: {x0.x}")
    print(f"Knishes: {int(x1.x)}")
else:
    print("No optimal solution found.")
```