To solve this problem using Gurobi, we will define variables and constraints based on the given conditions. The goal is to maximize the objective function which represents a combination of the number of items (oreos, green beans, potatoes, cherry pies, ravioli, and rotisserie chickens) multiplied by their respective coefficients.

Let's denote:
- \(O\) as the number of oreos,
- \(G\) as the number of green beans,
- \(P\) as the number of potatoes,
- \(C\) as the number of cherry pies,
- \(R\) as the number of ravioli,
- \(RC\) as the number of rotisserie chickens.

First, we'll import necessary modules and define our variables and model. Then, we will add constraints based on the given conditions.

```python
from gurobipy import *

# Create a model
m = Model("Food Optimization")

# Define variables - since non-integer amounts are allowed for all items, we use continuous variables.
O = m.addVar(vtype=GRB.CONTINUOUS, name="oreos")
G = m.addVar(vtype=GRB.CONTINUOUS, name="green_beans")
P = m.addVar(vtype=GRB.CONTINUOUS, name="potatoes")
C = m.addVar(vtype=GRB.CONTINUOUS, name="cherry_pies")
R = m.addVar(vtype=GRB.CONTINUOUS, name="ravioli")
RC = m.addVar(vtype=GRB.CONTINUOUS, name="rotisserie_chickens")

# Objective function: Maximize the total value (example coefficients used)
m.setObjective(2*O + 3*G + P + C + R + RC, GRB.MAXIMIZE)

# Constraints based on given conditions
# Protein constraints (assuming 1 unit of protein per item for simplicity)
m.addConstr(G + P >= 11, "Green beans and potatoes constraint")
m.addConstr(O + G + RC >= 19, "Oreos, green beans, and rotisserie chickens constraint")

# Tastiness rating constraints
m.addConstr(9*G + 6*C >= 13, "Green beans and cherry pies tastiness")
m.addConstr(5*P + 20*RC >= 19, "Potatoes and rotisserie chickens tastiness")

# Other constraints
m.addConstr(10*G - 3*C >= 0, "Green beans and cherry pies quantity constraint")
m.addConstr(2*O - 10*C - 5*RC >= 0, "Oreos, cherry pies, and rotisserie chickens quantity constraint")

# Protein upper bounds
m.addConstr(G + C <= 126, "Green beans and cherry pies protein limit")
m.addConstr(G + RC <= 83, "Green beans and rotisserie chickens protein limit")
m.addConstr(R + RC <= 112, "Ravioli and rotisserie chickens protein limit")

# Tastiness upper bounds
m.addConstr(5*P + 14*R <= 85, "Potatoes and ravioli tastiness limit")
m.addConstr(9*G + 14*R <= 26, "Green beans and ravioli tastiness limit")

# Solve the model
m.optimize()

# Print solution
for v in m.getVars():
    print(f"{v.VarName}: {v.X}")
```

This code sets up a basic model to optimize the selection of items based on their protein content, tastiness ratings, and other constraints. Note that you may need to adjust coefficients in objective functions and constraints according to your specific problem requirements. Also, ensure that Gurobi is installed (`pip install gurobipy`) before running this script.