To solve this optimization problem, we will use the Gurobi Python API. The problem involves maximizing an objective function that is a linear combination of the quantities of potatoes, sashimi, and bowls of instant ramen, subject to various constraints related to their tastiness ratings and iron content.

First, let's break down the given information:

- Variables: `potatoes`, `sashimi`, `bowls_of_instant_ramen`
- Objective function to maximize: `6 * potatoes + 5 * sashimi + 2 * bowls_of_instant_ramen`
- Constraints:
  - Tastiness ratings and iron content for each food item.
  - Lower bounds on the total tastiness from certain combinations of foods.
  - Upper bounds on the total tastiness from other combinations.
  - Minimum and maximum amounts of iron to be obtained from specific combinations.

Given that `potatoes`, `sashimi`, and `bowls_of_instant_ramen` can all potentially be fractional (as per the problem statement), we will model them as continuous variables in Gurobi.

Here is how you could write this optimization problem using the Gurobi Python API:

```python
from gurobipy import *

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

# Define the decision variables
potatoes = m.addVar(vtype=GRB.CONTINUOUS, name="potatoes")
sashimi = m.addVar(vtype=GRB.CONTINUOUS, name="sashimi")
bowls_of_instant_ramen = m.addVar(vtype=GRB.CONTINUOUS, name="bowls_of_instant_ramen")

# Objective function: Maximize 6*potatoes + 5*sashimi + 2*bowls_of_instant_ramen
m.setObjective(6 * potatoes + 5 * sashimi + 2 * bowls_of_instant_ramen, GRB.MAXIMIZE)

# Constraints:
# - Total tastiness from sashimi and bowls of instant ramen >= 34
m.addConstr(12.31 * sashimi + 5.36 * bowls_of_instant_ramen >= 34, name="tastiness_sashimi_ramen")

# - Total tastiness from potatoes and bowls of instant ramen >= 61
m.addConstr(1.7 * potatoes + 5.36 * bowls_of_instant_ramen >= 61, name="tastiness_potatoes_ramen")

# - Total tastiness from all three foods >= 67
m.addConstr(1.7 * potatoes + 12.31 * sashimi + 5.36 * bowls_of_instant_ramen >= 67, name="total_tastiness_min")

# - Iron from potatoes and sashimi >= 100 mg
m.addConstr(10.23 * potatoes + 4.49 * sashimi >= 100, name="iron_potatoes_sashimi_min")

# - Total tastiness from potatoes and bowls of instant ramen <= 204
m.addConstr(1.7 * potatoes + 5.36 * bowls_of_instant_ramen <= 204, name="tastiness_potatoes_ramen_max")

# - Total tastiness from all three foods <= 204
m.addConstr(1.7 * potatoes + 12.31 * sashimi + 5.36 * bowls_of_instant_ramen <= 204, name="total_tastiness_max")

# - Iron from potatoes and bowls of instant ramen <= 163 mg
m.addConstr(10.23 * potatoes + 6.03 * bowls_of_instant_ramen <= 163, name="iron_potatoes_ramen_max")

# - Total iron from all foods <= 163 mg
m.addConstr(10.23 * potatoes + 4.49 * sashimi + 6.03 * bowls_of_instant_ramen <= 163, name="total_iron_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Potatoes: {potatoes.x}")
    print(f"Sashimi: {sashimi.x}")
    print(f"Bowls of instant ramen: {bowls_of_instant_ramen.x}")
else:
    print("No optimal solution found")
```