To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (steaks, pickles, strawberries, bowls of pasta), formulating the objective function using these variables, and then listing all constraints in terms of these variables.

Let's define the variables as follows:
- $x_1$ represents the number of steaks,
- $x_2$ represents the number of pickles,
- $x_3$ represents the quantity of strawberries,
- $x_4$ represents the quantity of bowls of pasta.

The objective function to minimize is: $7.17x_1 + 5.44x_2 + 5.24x_3 + 3.15x_4$.

Constraints:
1. Spend at least $39 on steaks plus strawberries: $2x_1 + 2x_3 \geq 39$
2. Spend at least $46 on steaks and bowls of pasta: $2x_1 + 2x_4 \geq 46$
3. Spend at least $40 on steaks and pickles: $2x_1 + 13x_2 \geq 40$
4. Spend at least $40 on steaks, pickles, strawberries, and bowls of pasta: $2x_1 + 13x_2 + 2x_3 + 2x_4 \geq 40$
5. $3x_1 - 10x_2 \geq 0$
6. Spend at most $128 on steaks, pickles, and bowls of pasta: $2x_1 + 13x_2 + 2x_4 \leq 128$

The symbolic representation in JSON format is:
```json
{
    'sym_variables': [('x1', 'steaks'), ('x2', 'pickles'), ('x3', 'strawberries'), ('x4', 'bowls of pasta')],
    'objective_function': '7.17*x1 + 5.44*x2 + 5.24*x3 + 3.15*x4',
    'constraints': [
        '2*x1 + 2*x3 >= 39',
        '2*x1 + 2*x4 >= 46',
        '2*x1 + 13*x2 >= 40',
        '2*x1 + 13*x2 + 2*x3 + 2*x4 >= 40',
        '3*x1 - 10*x2 >= 0',
        '2*x1 + 13*x2 + 2*x4 <= 128'
    ]
}
```

Given this problem, we can now express it in Gurobi code. Since the variables can be fractional (continuous), we will use `GRB.CONTINUOUS` for all variables.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="steaks")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="pickles")
x3 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="strawberries")
x4 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bowls_of_pasta")

# Define the objective function
m.setObjective(7.17*x1 + 5.44*x2 + 5.24*x3 + 3.15*x4, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + 2*x3 >= 39, name="steaks_and_strawberries")
m.addConstr(2*x1 + 2*x4 >= 46, name="steaks_and_pasta")
m.addConstr(2*x1 + 13*x2 >= 40, name="steaks_and_pickles")
m.addConstr(2*x1 + 13*x2 + 2*x3 + 2*x4 >= 40, name="all_items_together")
m.addConstr(3*x1 - 10*x2 >= 0, name="steaks_vs_pickles")
m.addConstr(2*x1 + 13*x2 + 2*x4 <= 128, name="budget_for_steaks_pickles_pasta")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    for var in m.getVars():
        print(f"{var.VarName}: {var.x}")
else:
    print("No optimal solution found")
```