To solve this optimization problem, we need to first define the symbolic representation of the variables and the objective function. Then, we will formulate the constraints based on the given conditions.

The variables are:
- `x0`: bagged salads
- `x1`: potatoes

The objective function is to minimize: 
`1*x0 + 4*x1`

The constraints are:
1. Calcium constraint (at least 39 milligrams): `4.0*x0 + 0.84*x1 >= 39`
2. Sourness index constraint (at least 59): `12.96*x0 + 15.92*x1 >= 59`
3. Another calcium constraint which seems to be a repetition of the first one, so we ignore it for being redundant.
4. Another sourness index constraint which also seems to be a repetition, thus ignored.
5. The linear combination constraint: `-5*x0 + 8*x1 >= 0`
6. Upper bound on calcium (no more than 100 milligrams): `4.0*x0 + 0.84*x1 <= 100`
7. Upper bound on sourness index (at most 80): `12.96*x0 + 15.92*x1 <= 80`

Given the fractional nature of both variables, we do not need to specify them as integers.

Here is the symbolic representation in JSON format:
```json
{
    'sym_variables': [('x0', 'bagged salads'), ('x1', 'potatoes')],
    'objective_function': 'minimize 1*x0 + 4*x1',
    'constraints': [
        '4.0*x0 + 0.84*x1 >= 39', 
        '12.96*x0 + 15.92*x1 >= 59', 
        '-5*x0 + 8*x1 >= 0', 
        '4.0*x0 + 0.84*x1 <= 100', 
        '12.96*x0 + 15.92*x1 <= 80'
    ]
}
```

Now, let's write the Gurobi code for this problem:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(1*x0 + 4*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(4.0*x0 + 0.84*x1 >= 39, "calcium_lower_bound")
m.addConstr(12.96*x0 + 15.92*x1 >= 59, "sourness_lower_bound")
m.addConstr(-5*x0 + 8*x1 >= 0, "linear_combination_constraint")
m.addConstr(4.0*x0 + 0.84*x1 <= 100, "calcium_upper_bound")
m.addConstr(12.96*x0 + 15.92*x1 <= 80, "sourness_upper_bound")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Bagged Salads: {x0.x}")
    print(f"Potatoes: {x1.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```