To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints using these variables.

Let's denote:
- \(x_1\) as the amount of fertilizer P100 used in kilograms.
- \(x_2\) as the amount of fertilizer Y200 used in kilograms.

The objective is to minimize the total amount of vitamin B in the compound. Given that each kg of P100 contains 4 units of vitamin B and each kg of Y200 contains 6 units of vitamin B, the objective function can be represented as:
\[ \text{Minimize:} \quad 4x_1 + 6x_2 \]

The constraints based on the requirements are:
1. Minimum 200 units of nitrogen: \(11x_1 + 9x_2 \geq 200\)
2. Minimum 150 units of phosphoric acid: \(6x_1 + 10x_2 \geq 150\)
3. No more than 300 units of vitamin A: \(5x_1 + 8x_2 \leq 300\)

Additionally, since we cannot use a negative amount of fertilizer, we have non-negativity constraints:
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'amount of P100 in kg'), ('x2', 'amount of Y200 in kg')],
    'objective_function': '4*x1 + 6*x2',
    'constraints': [
        '11*x1 + 9*x2 >= 200',
        '6*x1 + 10*x2 >= 150',
        '5*x1 + 8*x2 <= 300',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

To solve this problem using Gurobi in Python, we can use the following code:
```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(name="P100", lb=0)
x2 = m.addVar(name="Y200", lb=0)

# Set the objective function
m.setObjective(4*x1 + 6*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(11*x1 + 9*x2 >= 200, name="Nitrogen_Constraint")
m.addConstr(6*x1 + 10*x2 >= 150, name="Phosphoric_Acid_Constraint")
m.addConstr(5*x1 + 8*x2 <= 300, name="Vitamin_A_Constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"P100: {x1.x} kg")
    print(f"Y200: {x2.x} kg")
    print(f"Minimum Vitamin B: {4*x1.x + 6*x2.x} units")
else:
    print("No optimal solution found.")
```