## Step 1: Define the symbolic representation of the problem
Let's denote the amount of fertilizer P100 to be used as $x_1$ kg and the amount of fertilizer Y200 to be used as $x_2$ kg. The objective is to minimize the amount of vitamin B in the compound. Each kg of fertilizer P100 contains 4 units of vitamin B, and each kg of fertilizer Y200 contains 6 units of vitamin B. Therefore, the objective function to minimize is $4x_1 + 6x_2$.

## Step 2: Define the constraints based on the problem description
The plant growth compound requires:
- A minimum of 200 units of nitrogen: $11x_1 + 9x_2 \geq 200$
- A minimum of 150 units of phosphoric acid: $6x_1 + 10x_2 \geq 150$
- No more than 300 units of vitamin A: $5x_1 + 8x_2 \leq 300$
Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the amount of fertilizers cannot be negative.

## 3: Symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'kg of fertilizer P100'), ('x2', 'kg of fertilizer Y200')],
    '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'
    ]
}
```

## 4: Convert the problem into Gurobi code in Python
```python
import gurobipy as gp

# Create a new model
m = gp.Model("PlantGrowthCompound")

# Define the variables
x1 = m.addVar(name="P100", lb=0)  # kg of fertilizer P100
x2 = m.addVar(name="Y200", lb=0)  # kg of fertilizer Y200

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

# Define the constraints
m.addConstr(11*x1 + 9*x2 >= 200, name="Nitrogen_Requirement")
m.addConstr(6*x1 + 10*x2 >= 150, name="Phosphoric_Acid_Requirement")
m.addConstr(5*x1 + 8*x2 <= 300, name="Vitamin_A_Limit")

# Solve the model
m.optimize()

# Print the results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal amount of P100: {x1.varValue} kg")
    print(f"Optimal amount of Y200: {x2.varValue} kg")
    print(f"Minimum amount of vitamin B: {4*x1.varValue + 6*x2.varValue} units")
else:
    print("The model is infeasible.")
```