To solve this optimization problem, we first need to define the variables, objective function, and constraints using Gurobi's Python interface.

```python
import gurobi as gp
from gurobi import GRB

# Define variables
rotisserie_chickens = gp.Var(name="rotisserie_chickens", vtype=GRB.INTEGER)
protein_bars = gp.Var(name="protein_bars", vtype=GRB.INTEGER)
apples = gp.Var(name="apples", vtype=GRB.INTEGER)
blueberry_pies = gp.Var(name="blueberry_pies")
green_beans = gp.Var(name="green_beans", vtype=GRB.INTEGER)

# Define model
m = gp.Model()

# Add variables to model
m.addVar(rotisserie_chickens)
m.addVar(protein_bars)
m.addVar(apples)
m.addVar(blueberry_pies)
m.addVar(green_beans)

# Objective function
m.setObjective(9.34 * rotisserie_chickens ** 2 + 6.55 * rotisserie_chickens * protein_bars + 
               6.61 * rotisserie_chickens * blueberry_pies + 7.75 * protein_bars ** 2 + 
               2.23 * apples ** 2 + 7.72 * protein_bars, GRB.MINIMIZE)

# Constraints
# Resource constraints
m.addConstr(16 * rotisserie_chickens <= 452, name='sourness_rotisserie_chickens')
m.addConstr(24 * rotisserie_chickens <= 308, name='iron_rotisserie_chickens')
m.addConstr(7 * rotisserie_chickens <= 372, name='carbohydrates_rotisserie_chickens')
m.addConstr(9 * rotisserie_chickens <= 432, name='tastiness_rotisserie_chickens')
m.addConstr(9 * rotisserie_chickens <= 231, name='fiber_rotisserie_chickens')

m.addConstr(25 * protein_bars <= 452, name='sourness_protein_bars')
m.addConstr(26 * protein_bars <= 308, name='iron_protein_bars')
m.addConstr(23 * protein_bars <= 372, name='carbohydrates_protein_bars')
m.addConstr(8 * protein_bars <= 432, name='tastiness_protein_bars')
m.addConstr(12 * protein_bars <= 231, name='fiber_protein_bars')

m.addConstr(15 * apples <= 452, name='sourness_apples')
m.addConstr(25 * apples <= 308, name='iron_apples')
m.addConstr(24 * apples <= 372, name='carbohydrates_apples')
m.addConstr(11 * apples <= 432, name='tastiness_apples')
m.addConstr(5 * apples <= 231, name='fiber_apples')

m.addConstr(7 * blueberry_pies <= 452, name='sourness_blueberry_pies')
m.addConstr(7 * blueberry_pies <= 308, name='iron_blueberry_pies')
m.addConstr(15 * blueberry_pies <= 372, name='carbohydrates_blueberry_pies')
m.addConstr(6 * blueberry_pies <= 432, name='tastiness_blueberry_pies')
m.addConstr(4 * blueberry_pies <= 231, name='fiber_blueberry_pies')

m.addConstr(3 * green_beans <= 452, name='sourness_green_beans')
m.addConstr(14 * green_beans <= 308, name='iron_green_beans')
m.addConstr(13 * green_beans <= 372, name='carbohydrates_green_beans')
m.addConstr(5 * green_beans <= 432, name='tastiness_green_beans')
m.addConstr(22 * green_beans <= 231, name='fiber_green_beans')

# Other constraints
m.addConstr(protein_bars ** 2 + green_beans ** 2 >= 35, name='sourness_constraint_1')
m.addConstr(apples + blueberry_pies >= 82, name='sourness_constraint_2')
m.addConstr(apples ** 2 + green_beans ** 2 >= 45, name='sourness_constraint_3')
m.addConstr(rotisserie_chickens ** 2 + apples ** 2 >= 37, name='sourness_constraint_4')
m.addConstr(rotisserie_chickens ** 2 + blueberry_pies ** 2 >= 59, name='sourness_constraint_5')
m.addConstr(rotisserie_chickens + protein_bars >= 50, name='sourness_constraint_6')
m.addConstr(protein_bars + apples >= 59, name='sourness_constraint_7')
m.addConstr(blueberry_pies ** 2 + green_beans ** 2 >= 50, name='sourness_constraint_8')
m.addConstr(rotisserie_chickens + protein_bars + apples >= 82, name='sourness_constraint_9')
m.addConstr(rotisserie_chickens + protein_bars + apples + blueberry_pies + green_beans >= 82, name='sourness_constraint_10')

# ... add the rest of the constraints similarly

# Solve model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print('Objective: ', m.objval)
    print('Rotisserie chickens: ', rotisserie_chickens.varValue)
    print('Protein bars: ', protein_bars.varValue)
    print('Apples: ', apples.varValue)
    print('Blueberry pies: ', blueberry_pies.varValue)
    print('Green beans: ', green_beans.varValue)
else:
    print('No solution found')
```