## Problem Description and Formulation

The problem is an optimization problem where we need to maximize the objective function:

\[ 3.92 \times \text{oranges} + 5.07 \times \text{bagged salads} \]

subject to several constraints related to the nutritional content of oranges and bagged salads.

## Constraints

1. **Iron Content**: 
   - Oranges contain 1 milligram of iron.
   - Bagged salads contain 2 milligrams of iron.
   - Total iron content must be at least 13 milligrams and at most 27 milligrams.

2. **Fat Content**:
   - Oranges contain 4 grams of fat.
   - Bagged salads contain 5 grams of fat.
   - Total fat content must be at least 6 grams and at most 16 grams.

3. **Protein Content**:
   - Oranges contain 5 grams of protein.
   - Bagged salads contain 3 grams of protein.
   - Total protein content must be at least 8 grams and at most 11 grams.

4. **Linear Constraint**:
   - \(-1 \times \text{oranges} + 10 \times \text{bagged salads} \geq 0\)

5. **Variable Constraints**:
   - Oranges can be a non-integer amount.
   - Bagged salads must be an integer amount.

## Gurobi Code Formulation

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    oranges = model.addVar(lb=0, ub=None, vtype=gurobi.GRB.CONTINUOUS, name="oranges")
    bagged_salads = model.addVar(lb=0, vtype=gurobi.GRB.INTEGER, name="bagged_salads")

    # Objective function
    model.setObjective(3.92 * oranges + 5.07 * bagged_salads, gurobi.GRB.MAXIMIZE)

    # Constraints
    # Iron content constraints
    model.addConstr(oranges + 2 * bagged_salads >= 13, name="min_iron")
    model.addConstr(oranges + 2 * bagged_salads <= 27, name="max_iron")

    # Fat content constraints
    model.addConstr(4 * oranges + 5 * bagged_salads >= 6, name="min_fat")
    model.addConstr(4 * oranges + 5 * bagged_salads <= 16, name="max_fat")

    # Protein content constraints
    model.addConstr(5 * oranges + 3 * bagged_salads >= 8, name="min_protein")
    model.addConstr(5 * oranges + 3 * bagged_salads <= 11, name="max_protein")

    # Linear constraint
    model.addConstr(-oranges + 10 * bagged_salads >= 0, name="linear_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Oranges: {oranges.varValue}")
        print(f"Bagged Salads: {bagged_salads.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("The model is infeasible.")

solve_optimization_problem()
```