To solve the given optimization problem using Gurobi, we first need to define the variables and constraints based on the provided information. The objective function is to maximize the value of 2 times the amount of bowls of pasta plus 9 times the total number of apples plus 5 multiplied by the amount of hot dogs.

Let's denote:
- `p` as the number of bowls of pasta,
- `a` as the number of apples, and
- `h` as the number of hot dogs.

The constraints can be formulated based on the given resources/attributes and requirements. For instance, the iron content constraint from bowls of pasta, apples, and hot dogs should not exceed 65 milligrams, among other constraints.

Below is the Gurobi code that captures the problem description:

```python
from gurobipy import *

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

# Define variables
p = m.addVar(lb=0, name="bowls_of_pasta")  # Continuous variable for bowls of pasta
a = m.addVar(lb=0, name="apples")          # Continuous variable for apples
h = m.addVar(vtype=GRB.INTEGER, lb=0, name="hot_dogs")  # Integer variable for hot dogs

# Objective function: Maximize 2*p + 9*a + 5*h
m.setObjective(2*p + 9*a + 5*h, GRB.MAXIMIZE)

# Constraints
# Iron constraints
m.addConstr(p*7 + a*6 + h*9 >= 13, name="iron_min")  # At least 13 milligrams of iron
m.addConstr(p*7 + h*9 <= 25, name="iron_max_bowls_hotdogs")  # No more than 25 mg from bowls and hot dogs
m.addConstr(p*7 + a*6 + h*9 <= 65, name="total_iron_max")  # Total iron constraint

# Carbohydrates constraints
m.addConstr(a*7 + h*7 >= 17, name="carbs_min_apples_hotdogs")  # At least 17 grams from apples and hot dogs
m.addConstr(p*4 + h*7 >= 15, name="carbs_min_bowls_hotdogs")  # At least 15 grams from bowls and hot dogs
m.addConstr(p*4 + a*7 + h*7 <= 93, name="total_carbs_max")  # Total carbohydrates constraint

# Fiber constraints
m.addConstr(a*7 + h*2 >= 22, name="fiber_min_apples_hotdogs")  # At least 22 grams from apples and hot dogs
m.addConstr(p*11 + h*2 >= 18, name="fiber_min_bowls_hotdogs")  # At least 18 grams from bowls and hot dogs
m.addConstr(p*11 + a*7 <= 100, name="fiber_max_bowls_apples")  # No more than 100 grams from bowls and apples
m.addConstr(p*11 + h*2 <= 84, name="fiber_max_bowls_hotdogs")  # No more than 84 grams from bowls and hot dogs
m.addConstr(p*11 + a*7 + h*2 <= 130, name="total_fiber_max")  # Total fiber constraint

# Cost constraints
m.addConstr(a*10 + h*7 <= 73, name="cost_max_apples_hotdogs")  # Maximum cost for apples and hot dogs
m.addConstr(p*10 + a*10 + h*7 <= 91, name="total_cost_max")  # Total cost constraint

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Bowls of pasta: {p.x}")
    print(f"Apples: {a.x}")
    print(f"Hot dogs: {h.x}")
else:
    print("No optimal solution found")
```

This code defines the variables `p`, `a`, and `h` for bowls of pasta, apples, and hot dogs, respectively, with `p` and `a` being continuous and `h` being an integer variable. It then sets up the objective function to maximize and adds all the given constraints before optimizing the model. The solution, if found optimal, is printed out along with the values for each of the variables.