To solve this optimization problem using Gurobi, we first need to understand and translate the given constraints and objective function into a mathematical model. The variables are 'milkshakes', 'cherry pies', 'ravioli', and 'bananas'. We aim to minimize the objective function: 2*milkshakes + 8*cherry_pies + 5*ravioli + 7*bananas.

The constraints provided can be grouped into two categories based on their resource attributes (umami index and grams of carbohydrates) and other additional constraints. Given that all variables are allowed to be fractional, we do not need to worry about integer constraints for any of them.

Here is the breakdown of how we'll approach this in Gurobi:

1. **Define Variables**: We define our decision variables as `milkshakes`, `cherry_pies`, `ravioli`, and `bananas`.

2. **Objective Function**: Minimize 2*milkshakes + 8*cherry_pies + 5*ravioli + 7*bananas.

3. **Constraints**:
   - Umami index constraints for each food item.
   - Carbohydrate content constraints for each combination of items as specified.
   - Minimum and maximum total umami indexes from various combinations of food items.
   - Constraints on the number of ravioli and bananas, milkshakes and cherry pies.

Given these considerations, we can now formulate our problem in Gurobi. Here is how you might set it up:

```python
from gurobipy import *

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

# Define variables
milkshakes = model.addVar(lb=0, name="milkshakes")
cherry_pies = model.addVar(lb=0, name="cherry_pies")
ravioli = model.addVar(lb=0, name="ravioli")
bananas = model.addVar(lb=0, name="bananas")

# Set the objective function
model.setObjective(2*milkshakes + 8*cherry_pies + 5*ravioli + 7*bananas, GRB.MINIMIZE)

# Add constraints
# Umami index and carbohydrate content constraints
model.addConstr(22*milkshakes + 10*cherry_pies + 23*ravioli + 3*bananas >= 28, name="umami_total_min")
model.addConstr(10*cherry_pies + 3*bananas >= 44, name="umami_cherry_banana_min")
model.addConstr(10*cherry_pies + 23*ravioli >= 20, name="umami_cherry_ravioli_min")
model.addConstr(22*milkshakes + 23*ravioli >= 32, name="umami_milkshake_ravioli_min")
model.addConstr(22*milkshakes + 3*bananas >= 28, name="umami_milkshake_banana_min")

# Carbohydrate constraints
model.addConstr(2*milkshakes + 11*ravioli >= 17, name="carb_milkshake_ravioli_min")
model.addConstr(9*cherry_pies + 12*bananas >= 20, name="carb_cherry_banana_min")
model.addConstr(2*milkshakes + 9*cherry_pies >= 25, name="carb_milkshake_cherry_min")
model.addConstr(11*ravioli + 12*bananas >= 16, name="carb_ravioli_banana_min")
model.addConstr(9*cherry_pies + 11*ravioli + 12*bananas >= 27, name="carb_cherry_ravioli_banana_min")
model.addConstr(2*milkshakes + 9*cherry_pies + 11*ravioli + 12*bananas >= 27, name="carb_all_min")

# Other constraints
model.addConstr(10*ravioli - 8*bananas >= 0, name="ravioli_banana_constraint")
model.addConstr(milkshakes - 3*cherry_pies >= 0, name="milkshake_cherry_constraint")

# Maximum umami index constraints
model.addConstr(23*ravioli + 3*bananas <= 163, name="umami_ravioli_banana_max")
model.addConstr(10*cherry_pies + 23*ravioli <= 203, name="umami_cherry_ravioli_max")
model.addConstr(22*milkshakes + 3*bananas <= 62, name="umami_milkshake_banana_max")

# Maximum carbohydrate constraint
model.addConstr(2*milkshakes + 11*ravioli + 12*bananas <= 44, name="carb_milkshake_ravioli_banana_max")

# Optimize model
model.optimize()

```python 
```