To solve the problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables for the quantities of oats and flaxseed to be grown, formulating the objective function that represents the total profit, and translating the constraints into mathematical expressions.

Let's define:
- \(x_1\) as the number of acres of oats to grow,
- \(x_2\) as the number of acres of flaxseed to grow.

The objective function, which is to maximize the total profit, can be represented as:
\[500x_1 + 400x_2\]

Given the constraints:
1. The farmer has 50 acres of land in total: \(x_1 + x_2 \leq 50\)
2. He must grow at least 5 acres of oats: \(x_1 \geq 5\)
3. He must grow at least 8 acres of flaxseed: \(x_2 \geq 8\)
4. The amount of oats cannot exceed twice the amount of flaxseed: \(x_1 \leq 2x_2\)

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'acres of oats'), ('x2', 'acres of flaxseed')],
    'objective_function': '500*x1 + 400*x2',
    'constraints': [
        'x1 + x2 <= 50',
        'x1 >= 5',
        'x2 >= 8',
        'x1 <= 2*x2'
    ]
}
```

To find the solution using Gurobi in Python, we can use the following code:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="oats", lb=0)
x2 = m.addVar(name="flaxseed", lb=0)

# Set the objective function
m.setObjective(500*x1 + 400*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 50, name="total_land")
m.addConstr(x1 >= 5, name="min_oats")
m.addConstr(x2 >= 8, name="min_flaxseed")
m.addConstr(x1 <= 2*x2, name="oats_vs_flaxseed")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of oats: {x1.x}")
    print(f"Acres of flaxseed: {x2.x}")
    print(f"Maximum profit: ${500*x1.x + 400*x2.x:.2f}")
else:
    print("No optimal solution found")
```