## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to various constraints. The variables are:
- $x_0$: chicken thighs
- $x_1$: potatoes
- $x_2$: tomatoes
- $x_3$: knishes
- $x_4$: bananas
- $x_5$: green beans

## Step 2: Formulate the objective function
The objective function to maximize is:
\[ 1.36x_0^2 + 4.33x_0x_1 + 9.9x_1x_2 + 9.03x_2x_4 + 8.22x_2x_5 + 7.37x_3x_5 + 6.95x_4x_5 + 1.11x_0 + 6.63x_1 + 2.13x_2 + 2.25x_3 \]

## 3: List the constraints
Constraints include:
- Resource constraints:
  - $8.49x_0 + 6.78x_1 + 13.22x_2 + 1.16x_3 + 9.54x_4 + 14.58x_5 \leq 343$ (iron)
  - $12.04x_0 + 8.45x_1 + 13.92x_2 + 11.57x_3 + 0.58x_4 + 6.75x_5 \leq 420$ (calcium)
  - $11.84x_0 + 1.74x_1 + 8.63x_2 + 0.85x_3 + 3.88x_4 + 12.18x_5 \leq 270$ (cost)
- Iron constraints:
  - $8.49x_0^2 + 13.22x_2^2 \geq 54$
  - ...
- Calcium constraints:
  - $13.92x_2 + 11.57x_3 + 0.58x_4 \geq 52$
  - ...
- Cost constraints:
  - $8.63x_2 + 0.85x_3 + 3.88x_4 \geq 26$
  - ...

## 4: Convert to Gurobi code
```python
import gurobi as gp

# Define variables
x = gp.mvar(6, name="x", lb=0)

# Define model
model = gp.Model()

# Objective function
model.setObjective(1.36*x[0]**2 + 4.33*x[0]*x[1] + 9.9*x[1]*x[2] + 9.03*x[2]*x[4] + 8.22*x[2]*x[5] + 
                   7.37*x[3]*x[5] + 6.95*x[4]*x[5] + 1.11*x[0] + 6.63*x[1] + 2.13*x[2] + 2.25*x[3], 
                   sense=gp.GRB.MAXIMIZE)

# Constraints
# Resource constraints
model.addConstr(8.49*x[0] + 6.78*x[1] + 13.22*x[2] + 1.16*x[3] + 9.54*x[4] + 14.58*x[5] <= 343)
model.addConstr(12.04*x[0] + 8.45*x[1] + 13.92*x[2] + 11.57*x[3] + 0.58*x[4] + 6.75*x[5] <= 420)
model.addConstr(11.84*x[0] + 1.74*x[1] + 8.63*x[2] + 0.85*x[3] + 3.88*x[4] + 12.18*x[5] <= 270)

# Iron constraints
model.addConstr(8.49*x[0]**2 + 13.22*x[2]**2 >= 54)

# Calcium constraints
model.addConstr(13.92*x[2] + 11.57*x[3] + 0.58*x[4] >= 52)

# Cost constraints
model.addConstr(8.63*x[2] + 0.85*x[3] + 3.88*x[4] >= 26)

# Solve model
model.optimize()

# Print solution
if model.status == gp.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    for i in range(6):
        print(f"x[{i}] = {x[i].varValue}")
else:
    print("No optimal solution found")
```

The final answer is: 
```json
{
    'sym_variables': [
        ('x0', 'chicken thighs'), 
        ('x1', 'potatoes'), 
        ('x2', 'tomatoes'), 
        ('x3', 'knishes'), 
        ('x4', 'bananas'), 
        ('x5', 'green beans')
    ], 
    'objective_function': '1.36*x0^2 + 4.33*x0*x1 + 9.9*x1*x2 + 9.03*x2*x4 + 8.22*x2*x5 + 7.37*x3*x5 + 6.95*x4*x5 + 1.11*x0 + 6.63*x1 + 2.13*x2 + 2.25*x3', 
    'constraints': [
        '8.49*x0 + 6.78*x1 + 13.22*x2 + 1.16*x3 + 9.54*x4 + 14.58*x5 <= 343', 
        '12.04*x0 + 8.45*x1 + 13.92*x2 + 11.57*x3 + 0.58*x4 + 6.75*x5 <= 420', 
        '11.84*x0 + 1.74*x1 + 8.63*x2 + 0.85*x3 + 3.88*x4 + 12.18*x5 <= 270', 
        '8.49*x0^2 + 13.22*x2^2 >= 54', 
        '13.92*x2 + 11.57*x3 + 0.58*x4 >= 52', 
        '8.63*x2 + 0.85*x3 + 3.88*x4 >= 26'
    ]
}
```