To solve this optimization problem using Gurobi, we first need to define the variables and their bounds. The variables in this problem are 'oranges', 'fruit salads', and 'bowls of cereal'. Since these can be non-integer amounts, we will model them as continuous variables.

Next, we'll define the objective function and the constraints based on the given information. The objective is to maximize \(2 \times \text{oranges} + 9 \times \text{fruit salads} + 9 \times \text{bowls of cereal}\).

The constraints are based on the nutritional content and other requirements:

1. Carbohydrates from oranges, fruit salads, and bowls of cereal.
2. Iron from these items.
3. Umami index contributions.
4. Calcium intake.

We'll translate each constraint into a linear inequality or equality as needed.

```python
from gurobipy import *

# Create a model
m = Model("Nutrition_Optimization")

# Define variables
oranges = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="oranges")
fruit_salads = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="fruit_salads")
bowls_of_cereal = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bowls_of_cereal")

# Objective function: Maximize 2*oranges + 9*fruit_salads + 9*bowls_of_cereal
m.setObjective(2*oranges + 9*fruit_salads + 9*bowls_of_cereal, GRB.MAXIMIZE)

# Constraints
# At least 26 grams of carbohydrates from oranges and bowls of cereal
m.addConstr(4*oranges + 4*bowls_of_cereal >= 26, name="carbs_orange_cereal_min")

# At least 13 milligrams of iron from oranges plus bowls of cereal
m.addConstr(oranges + 2*bowls_of_cereal >= 13, name="iron_orange_cereal_min")

# At least 18 milligrams of iron from all sources
m.addConstr(oranges + 8*fruit_salads + 2*bowls_of_cereal >= 18, name="iron_all_min")

# Total umami index constraints
m.addConstr(7*fruit_salads + bowls_of_cereal >= 19, name="umami_fruit_cereal_min")
m.addConstr(8*oranges + bowls_of_cereal >= 11, name="umami_orange_cereal_min")

# Maximum carbohydrate constraints
m.addConstr(4*oranges + fruit_salads <= 54, name="carbs_orange_fruit_max")
m.addConstr(4*oranges + 4*bowls_of_cereal <= 58, name="carbs_orange_cereal_max")
m.addConstr(4*oranges + fruit_salads + 4*bowls_of_cereal <= 58, name="carbs_all_max")

# Maximum iron constraints
m.addConstr(oranges + 8*fruit_salads <= 62, name="iron_orange_fruit_max")
m.addConstr(oranges + 2*bowls_of_cereal <= 49, name="iron_orange_cereal_max")
m.addConstr(oranges + 8*fruit_salads + 2*bowls_of_cereal <= 49, name="iron_all_max")

# Maximum umami index constraints
m.addConstr(8*oranges + 7*fruit_salads <= 60, name="umami_orange_fruit_max")
m.addConstr(8*oranges + bowls_of_cereal <= 34, name="umami_orange_cereal_max")
m.addConstr(7*fruit_salads + bowls_of_cereal <= 39, name="umami_fruit_cereal_max")
m.addConstr(8*oranges + 7*fruit_salads + bowls_of_cereal <= 39, name="umami_all_max")

# Maximum calcium constraints
m.addConstr(2*oranges + 8*bowls_of_cereal <= 80, name="calcium_orange_cereal_max")
m.addConstr(2*oranges + 6*fruit_salads + 8*bowls_of_cereal <= 80, name="calcium_all_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Oranges: {oranges.x}")
    print(f"Fruit Salads: {fruit_salads.x}")
    print(f"Bowls of Cereal: {bowls_of_cereal.x}")
    print(f"Objective value: {m.objVal}")
else:
    print("No optimal solution found")
```