To solve this optimization problem, we need to define variables and an objective function that captures the total cost of operation in both oceans while meeting the demand for fish, crab, and shrimp. Let's denote:

- $x$ as the number of days spent fishing in the Indian Ocean,
- $y$ as the number of days spent fishing in the Arctic Ocean.

The objective is to minimize the total operational cost, which can be represented by the function: $6000x + 9000y$.

We have constraints based on the demand for each seafood product:
1. For fish: $3.4x + 6y \geq 20$
2. For crab: $2.2x + 5y \geq 12$
3. For shrimp: $1.5x + 3y \geq 10$

Additionally, since the number of days cannot be negative, we have:
- $x \geq 0$
- $y \geq 0$

Given these constraints and the objective function, we can now formulate this problem in Gurobi code to find the optimal values for $x$ and $y$.

```python
from gurobipy import *

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

# Define variables
x = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="Indian_Ocean_Days")
y = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="Arctic_Ocean_Days")

# Set the objective function: minimize total operational cost
m.setObjective(6000*x + 9000*y, GRB.MINIMIZE)

# Add constraints for each seafood product demand
m.addConstr(3.4*x + 6*y >= 20, "Fish_Demand")
m.addConstr(2.2*x + 5*y >= 12, "Crab_Demand")
m.addConstr(1.5*x + 3*y >= 10, "Shrimp_Demand")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: x = {x.x}, y = {y.x}")
    print(f"Total operational cost: ${6000*x.x + 9000*y.x:.2f}")
else:
    print("No optimal solution found")
```