To solve this optimization problem, we first need to understand and translate the given natural language description into a symbolic representation that can be used in mathematical modeling. The variables are 'bagged salads' and 'ravioli', which will be represented symbolically as \(x_1\) and \(x_2\), respectively.

The objective function described is to minimize \(7 \times \text{quantity of bagged salads} + 9 \times \text{number of ravioli}\). This translates to minimizing \(7x_1 + 9x_2\).

Given the constraints:

1. The sourness index from bagged salads and ravioli must be at least 45.
2. The total fat content from both items must be at least 57 grams.
3. \(-10 \times \text{number of bagged salads} + 3 \times \text{number of ravioli} \geq 0\).
4. The maximum combined sourness index is 101.
5. The maximum total fat content is 106 grams.

The symbolic representation of the constraints, considering the attributes provided for each item ('sourness index' and 'grams of fat'), can be formulated as follows:

- \(14x_1 + 12x_2 \geq 45\) (Minimum sourness index constraint)
- \(14x_1 + 4x_2 \geq 57\) (Minimum fat content constraint)
- \(-10x_1 + 3x_2 \geq 0\) (Mixed constraint on quantities)
- \(14x_1 + 12x_2 \leq 101\) (Maximum sourness index constraint)
- \(14x_1 + 4x_2 \leq 106\) (Maximum fat content constraint)

Since the problem allows for non-integer amounts of both items, \(x_1\) and \(x_2\) are continuous variables.

The symbolic representation in JSON format is:

```json
{
    'sym_variables': [('x1', 'bagged salads'), ('x2', 'ravioli')],
    'objective_function': '7*x1 + 9*x2',
    'constraints': [
        '14*x1 + 12*x2 >= 45',
        '14*x1 + 4*x2 >= 57',
        '-10*x1 + 3*x2 >= 0',
        '14*x1 + 12*x2 <= 101',
        '14*x1 + 4*x2 <= 106'
    ]
}
```

To solve this problem using Gurobi, we will write the following Python code:

```python
from gurobipy import *

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

# Add variables to the model
x1 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bagged_salads")
x2 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="ravioli")

# Set the objective function
model.setObjective(7*x1 + 9*x2, GRB.MINIMIZE)

# Add constraints to the model
model.addConstr(14*x1 + 12*x2 >= 45, "min_sourness")
model.addConstr(14*x1 + 4*x2 >= 57, "min_fat")
model.addConstr(-10*x1 + 3*x2 >= 0, "mixed_constraint")
model.addConstr(14*x1 + 12*x2 <= 101, "max_sourness")
model.addConstr(14*x1 + 4*x2 <= 106, "max_fat")

# Optimize the model
model.optimize()

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